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6. Philosophy of Artificial Intelligence (Philosophy of Artificial Intelligence on PhilPapers)

• Can Machines Think? [200]
• The Turing Test [57]
• Godelian arguments [31]
• The Chinese Room [44]
• Machine Consciousness [37]
• Machine Mentality, Misc [31]
• Computation and Representation [61]
• Symbols and Symbol Systems [17]
• Computational Semantics [14]
• Implicit/Explicit Rules and Representations [10]
• AI without Representation? [10]
• Computation and Representation, Misc [10]
• Philosophy of Connectionism [101]
• Connectionism and Compositionality [20]
• Representation in Connectionism [20]
• Connectionism and Eliminativism [5]
• The Connectionist/Classical Debate [15]
• Subsymbolic Computation [5]
• Philosophy of Connectionism, Misc [22]
• Philosophy of Connectionism, Foundational Empirical Issues [14]
• Special Topics in AI [105]

• Cyborgs [0]
• Transhumanism [0]
• Cybernetics [1]
• Dynamical Systems [35]
• The Nature of AI [6]
• The Frame Problem [13]
• AI Methodology [18]
• Robotics [23]

• Computation and Physical Systems [49]
• Computation and Physical Systems, Misc [1]
• Analog and Digital Computation [8]
• Computers [1]
• Implementing Computations [3]
• Noncomputable Processes [0]
• Pancomputationalism [5]
• Quantum Computation [3]

• Philosophy of AI, General Works [1]
• Philosophy of AI, Misc [2]

• Stevan Harnad (1984). Verifying machines' minds.   (Abstract & more)
  Abstract: he question of the possibility of artificial consciousness is both very new and very old. It is new in the context of contemporary cognitive science and its concern with whether a machine can be conscious; it is old in the form of the mind/body problem and the "other minds" problem of philosophy. Contemporary enthusiasts proceed at their peril if they ignore or are ignorant of the false starts and blind alleys that the older thinkers have painfully worked through
• Michael Morreau & Sarit Kraus (1998). Syntactical Treatments of Propositional Attitudes.   (Abstract & more)
  Abstract: Syntactical treatments of propositional attitudes are attractive to artificial intelligence researchers. But results of Montague (1974) and Thomason (1980) seem to show that syntactical treatments are not viable. They show that if representation languages are sufficiently expressive, then axiom schemes characterizing knowledge and belief give rise to paradox. Des Rivières and Levesque (1988) characterize a class of sentences within which these schemes can safely be instantiated. These sentences do not quantify over the propositional objects of knowledge and belief. We argue that their solution is incomplete, and extend it by characterizing a more inclusive class of sentences over which the axiom schemes can safely range. Our sentences do quantify over propositional objects.

Can Machines Think?

The Turing Test

• Varol Akman & Patrick Blackburn (2000). Editorial: Alan Turing and artificial intelligence.   (Abstract & more)
  Abstract: Department of Computer Engineering, Bilkent University, 06533 Ankara, Turkey E-mail: akman@cs.bilkent.edu.tr; http://www.cs.bilkent.edu.tr/?akman..
  Additional links for this entry:
  http://citeseer.ist.psu.edu/377438.html
  http://portal.acm.org/citation.cfm?id=595992
  http://cogprints.ecs.soton.ac.uk/archive/00001058/00/intro.ps
  http://www.cs.bilkent.edu.tr/~akman/jour-papers/jolli/jolli2000.pdf
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1058
  http://www.springerlink.com/content/rm454307n5h9k066/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=269485&PDF=1
  http://www.springerlink.com/index/RM454307N5H9K066.pdf
  http://www.ingentaconnect.com/content/klu/jlli/2000/00000009/00000004/00269485
  
• John Barresi (1987). Prospects for the cyberiad: Certain limits on human self-knowledge in the cybernetic age.   (More)
  Additional links for this entry:
  http://www3.interscience.wiley.com/cgi-bin/fulltext/120019196/PDFSTART
  
• Ned Block (1981). Psychologism and behaviorism.   (Abstract & more)
  Abstract: Let psychologism be the doctrine that whether behavior is intelligent behavior depends on the character of the internal information processing that produces it. More specifically, I mean psychologism to involve the doctrine that two systems could have actual and potential behavior _typical_ of familiar intelligent beings, that the two systems could be exactly alike in their actual and potential behavior, and in their behavioral dispositions and capacities and counterfactual behavioral properties (i.e., what behaviors, behavioral dispositions, and behavioral capacities they would have exhibited had their stimuli differed)--the two systems could be alike in all these ways, yet there could be a difference in the information processing that mediates their stimuli and responses that determines that one is not at all intelligent while the other is fully intelligent
  Additional links for this entry:
  http://philosophy.wisc.edu/shapiro/Phil951/Block.pdf
  http://www.nyu.edu/gsas/dept/philo/faculty/block/papers/Psychologism.pdf
  http://www.nyu.edu/gsas/dept/philo/faculty/block/papers/Psychologism.htm
  http://links.jstor.org/sici?sici=0031-8108(198101)90:1<5:PAB>2.0.CO;2-J
  http://www.jstor.org/sici?sici=0031-8108(198101)90:1<5:PAB>2.0.CO;2-J
  http://www.jstor.org/stable/pdfplus/2184371.pdf
  
• Selmer Bringsjord (2000). Animals, zombanimals, and the total Turing test: The essence of artificial intelligence.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=595843.595993
  http://citeseer.ist.psu.edu/bringsjord00animals.html
  http://www.rpi.edu/~faheyj2/SB/SELPAP/ZOMBANIMALS/zombanimals2.pdf
  http://www.kluweronline.com/article.asp?PIPS=269486&PDF=1
  http://www.springerlink.com/index/TV852P4283211235.pdf
  http://www.ingentaconnect.com/content/klu/jlli/2000/00000009/00000004/00269486
  
• Selmer Bringsjord, P. Bello & David A. Ferrucci (2001). Creativity, the Turing test, and the (better) Lovelace test.   (Abstract & more)
  Abstract:   The Turing Test (TT) is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT (or at least restricted versions of this test) have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent (or system) A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=596904
  http://citeseer.ist.psu.edu/bringsjord00creativity.html
  http://www.rpi.edu/~faheyj2/SB/SELPAP/DARTMOUTH/lt3.pdf
  http://www.springerlink.com/content/content/nrvl130w46xj6g22/fulltext.pdf
  http://www.springerlink.com/content/nrvl130w46xj6g22/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319539&PDF=1
  http://www.springerlink.com/index/NRVL130W46XJ6G22.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000001/00319539
  
• Andrew Clifton (ms). Blind man's bluff and the Turing test.   (Abstract & more)
  Abstract: It seems plausible that under the conditions of the Turing test, congenitally blind people could nevertheless, with sufficient preparation, successfully represent themselves to remotely located interrogators as sighted. Having never experienced normal visual sensations, the successful blind player can prevail in this test only by playing a ‘lying game’—imitating the phenomenological claims of sighted people, in the absence of the qualitative visual experiences to which such statements purportedly refer. This suggests that a computer or robot might pass the Turing test in the same way, in the absence not only of visual experience, but qualitative consciousness in general. Hence, the standard Turing test does not provide a valid criterion for the presence of consciousness. A ‘sensorimetric’ version of the Turing test fares no better, for the apparent correlations we observe between cognitive functions and qualitative conscious experiences seems to be contingent, not necessary. We must therefore define consciousness not in terms of its causes and effects, but rather, in terms of the distinctive properties of its content, such as its possession of qualitative character and apparent intrinsic value—the property which confers upon consciousness its moral significance. As a means of determining whether or nor a machine is conscious, in this sense, an alternative to the standard Turing test is proposed
• B. Jack Copeland (2000). The Turing test.   (Abstract & more)
  Abstract:   Turing''s test has been much misunderstood. Recently unpublished material by Turing casts fresh light on his thinking and dispels a number of philosophical myths concerning the Turing test. Properly understood, the Turing test withstands objections that are popularly believed to be fatal
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=PA519&ots=blQICrgbeX&sig=4woWcfbL_fO5zip_aVT89VSM390
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=PA519&ots=rzjaTOxdpq&sig=slbbZKdPbjoJsVO44CwfWr3oSMo
  http://www.springerlink.com/content/content/w103433h4g273841/fulltext.pdf
  http://www.springerlink.com/content/w103433h4g273841/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319531&PDF=1
  http://www.springerlink.com/index/W103433H4G273841.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000004/00319531
  
• Dr Louis J. Cutrona, Jr (ms). Zombies in Searle's chinese room: Putting the Turing test to bed.   (Abstract & more)
  Abstract: Searle’s discussions over the years 1980-2004 of the implications of his “Chinese Room” Gedanken experiment are frustrating because they proceed from a correct assertion: (1) “Instantiating a computer program is never by itself a sufficient condition of intentionality;” and an incorrect assertion: (2) “The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program.” In this article, I describe how to construct a Gedanken zombie Chinese Room program that will pass the Turing test and at the same time unambiguously demonstrates the correctness of (1). I then describe how to construct a Gedanken Chinese brain program that will pass the Turing test, has a mind, and understands Chinese, thus demonstrating that (2) is incorrect. Searle’s instantiation of this program can and does produce intentionality. Searle’s longstanding ignorance of Chinese is simply irrelevant and always has been. I propose a truce and a plan for further exploration
  Additional links for this entry:
  http://cogprints.org/4636/1/TR%2D05%2D002.pdf
  
• Bruce Edmonds (2000). The constructability of artificial intelligence (as defined by the Turing test).   (Abstract & more)
  Abstract: The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently a TM that could pass the TT could be built, isattacked on the grounds that not all TMs are constructible in a plannedway. This observation points towards the importance of developmentalprocesses that use random elements (e.g., evolution), but in these casesit becomes problematic to call the result artificial. This hasimplications for the means by which intelligent agents could bedeveloped
  Additional links for this entry:
  http://cogprints.org/397/3/consai.pdf
  http://cogprints.org/397/0/consai.pdf
  
• B. Edmonds (ms). The constructability of artificial intelligence (as defined by the Turing test).   (Abstract & more)
  Abstract: The Turing Test, as originally specified, centres on the ability to perform a social role. The TT can seen as a test of an ability to enter into normal human social dynamics. In this light it seems unlikely that such an entity can be wholly designed in an `off-line' mode, but rather a considerable period of training in situ would be required. The argument that since we can pass the TT and our cognitive processes might be implemented as a TM that, in theory, an TM that could pass the TT could be built is attacked on the grounds that not all TMs are constructable in a planned way. This observation points towards the importance of developmental processes that include random elements (e.g. evolution), but in these cases it becomes problematic to call the result artificial
  Additional links for this entry:
  http://cogprints.org/397/0/consai.pdf
  
• Gerald J. Erion (2001). The cartesian test for automatism.   (Abstract & more)
  Abstract:   In Part V of his Discourse on the Method, Descartes introduces a test for distinguishing people from machines that is similar to the one proposed much later by Alan Turing. The Cartesian test combines two distinct elements that Keith Gunderson has labeled the language test and the action test. Though traditional interpretation holds that the action test attempts to determine whether an agent is acting upon principles, I argue that the action test is best understood as a test of common sense. I also maintain that this interpretation yields a stronger test than Turing's, and that contemporary artificial intelligence should consider using it as a guide for future research
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA4-PA241&ots=blQICrghbY&sig=dV16mfWZM_Qoj6YrtNA26fR1IZk
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=RA5-PA241&ots=rzjaTOxjmr&sig=MTJT29H2X-bBW0rfBOaTIEju52o
  http://www.springerlink.com/content/content/h814j04926567527/fulltext.pdf
  http://www.springerlink.com/content/h814j04926567527/fulltext.pdf
  http://www.springerlink.com/index/H814J04926567527.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000001/00319541
  
• Luciano Floridi (2005). Consciousness, agents and the knowledge game.   (Abstract & more)
  Abstract: This paper has three goals. The first is to introduce the “knowledge game”, a new, simple and yet powerful tool for analysing some intriguing philosophical questions. The second is to apply the knowledge game as an informative test to discriminate between conscious (human) and conscious-less agents (zombies and robots), depending on which version of the game they can win. And the third is to use a version of the knowledge game to provide an answer to Dretske’s question “how do you know you are not a zombie?”
  Additional links for this entry:
  http://philsci-archive.pitt.edu/archive/00002546/
  http://philsci-archive.pitt.edu/archive/00002546/01/caatkg.pdf
  http://www.springerlink.com/content/f52pq39812453221/fulltext.pdf
  http://www.springerlink.com/index/F52PQ39812453221.pdf
  http://www.ingentaconnect.com/content/klu/mind/2005/00000015/F0020003/00009005
  
• Luciano Floridi, Mariarosaria Taddeo & Matteo Turilli (2008). Turing’s Imitation Game: Still an Impossible Challenge for All Machines and Some Judges.   (Abstract & more)
  Abstract: An Evaluation of the 2008 Loebner Contest.
• Robert M. French (2000). Peeking behind the screen: The unsuspected power of the standard Turing test.   (Abstract & more)
  Abstract: No computer that had not experienced the world as we humans had could pass a rigorously administered standard Turing Test. We show that the use of “subcognitive” questions allows the standard Turing Test to indirectly probe the human subcognitive associative concept network built up over a lifetime of experience with the world. Not only can this probing reveal differences in cognitive abilities, but crucially, even differences in _physical aspects_ of the candidates can be detected. Consequently, it is unnecessary to propose even harder versions of the Test in which all physical and behavioral aspects of the two candidates had to be indistinguishable before allowing the machine to pass the Test. Any machine that passed the “simpler” symbols- in/symbols-out test as originally proposed by Turing would be intelligent. The problem is that, even in its original form, the Turing Test is already too hard and too anthropocentric for any machine that was not a physical, social, and behavioral carbon copy of ourselves to actually pass it. Consequently, the Turing Test, even in its standard version, is not a reasonable test for general machine intelligence. There is no need for an even stronger version of the Test
  Additional links for this entry:
  http://citeseer.ist.psu.edu/french00peeking.html
  http://www.u-bourgogne.fr/LEAD/people/french/peeking.pdf
  http://www.informaworld.com/index/24PHYLU4NX08NHQF.pdf
  http://www.ingentaconnect.com/content/tandf/teta/2000/00000012/00000003/art00006
  
• Robert M. French (1990). Subcognition and the limits of the Turing test.   (More)
  Additional links for this entry:
  http://philosophy.wisc.edu/Shapiro/Phil554/PAPERS/French.pdf
  http://links.jstor.org/sici?sici=0026-4423(199001)2:99:393<53:SATLOT>2.0.CO;2-E
  http://www.jstor.org/sici?sici=0026-4423(199001)2:99:393<53:SATLOT>2.0.CO;2-E
  http://www.jstor.org/stable/pdfplus/2254890.pdf
  
• Robert French (1996). The inverted Turing test: How a mindless program could pass it.   (Abstract & more)
  Abstract: This commentary attempts to show that the inverted Turing Test (Watt 1996) could be simulated by a standard Turing test and, most importantly, claims that a very simple program with no intelligence whatsoever could be written that would pass the inverted Turing test. For this reason, the inverted Turing test in its present form must be rejected
  Additional links for this entry:
  http://www.u-bourgogne.fr/LEAD/people/french/invturing.pdf
  
• Robert French (2000). The Turing test: The first fifty years.   (Abstract & more)
  Abstract: The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last fifty years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960's to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now
  Additional links for this entry:
  http://citeseer.ist.psu.edu/french00turing.html
  http://srsc.ulb.ac.be/cogsci/papers/TICS-French_turing.pdf
  http://www.u-bourgogne.fr/LEAD/people/french/TICS_turing.pdf
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=10689346&dopt=Citation
  http://www.ingentaconnect.com/content/els/13646613/2000/00000004/00000003/art01453
  
• Stevan Harnad & Itiel Dror (2006). Distributed cognition: Cognizing, autonomy and the Turing test.   (Abstract & more)
  Abstract: Some of the papers in this special issue distribute cognition between what is going on inside individual cognizers' heads and their outside worlds; others distribute cognition among different individual cognizers. Turing's criterion for cognition was individual, autonomous input/output capacity. It is not clear that distributed cognition could pass the Turing Test
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/12368/02/discintro.pdf
  http://dialnet.unirioja.es/servlet/articulo?codigo=2196285
  http://www.ingentaconnect.com/content/jbp/pc/2006/00000014/00000002/art00002
  
• Stevan Harnad (1995). Does mind piggyback on robotic and symbolic capacity?   (Abstract & more)
  Abstract: Cognitive science is a form of "reverse engineering" (as Dennett has dubbed it). We are trying to explain the mind by building (or explaining the functional principles of) systems that have minds. A "Turing" hierarchy of empirical constraints can be applied to this task, from t1, toy models that capture only an arbitrary fragment of our performance capacity, to T2, the standard "pen-pal" Turing Test (total symbolic capacity), to T3, the Total Turing Test (total symbolic plus robotic capacity), to T4 (T3 plus internal [neuromolecular] indistinguishability). All scientific theories are underdetermined by data. What is the right level of empirical constraint for cognitive theory? I will argue that T2 is underconstrained (because of the Symbol Grounding Problem and Searle's Chinese Room Argument) and that T4 is overconstrained (because we don't know what neural data, if any, are relevant). T3 is the level at which we solve the "other minds" problem in everyday life, the one at which evolution operates (the Blind Watchmaker is no mind-reader either) and the one at which symbol systems can be grounded in the robotic capacity to name and manipulate the objects their symbols are about. I will illustrate this with a toy model for an important component of T3 -- categorization -- using neural nets that learn category invariance by "warping" similarity space the way it is warped in human categorical perception: within-category similarities are amplified and between-category similarities are attenuated. This analog "shape" constraint is the grounding inherited by the arbitrarily shaped symbol that names the category and by all the symbol combinations it enters into. No matter how tightly one constrains any such model, however, it will always be more underdetermined than normal scientific and engineering theory. This will remain the ineliminable legacy of the mind/body problem
• Stevan Harnad (1994). Levels of functional equivalence in reverse bioengineering: The Darwinian Turing test for artificial life.   (Abstract & more)
  Abstract: Both Artificial Life and Artificial Mind are branches of what Dennett has called "reverse engineering": Ordinary engineering attempts to build systems to meet certain functional specifications, reverse bioengineering attempts to understand how systems that have already been built by the Blind Watchmaker work. Computational modelling (virtual life) can capture the formal principles of life, perhaps predict and explain it completely, but it can no more be alive than a virtual forest fire can be hot. In itself, a computational model is just an ungrounded symbol system; no matter how closely it matches the properties of what is being modelled, it matches them only formally, with the mediation of an interpretation. Synthetic life is not open to this objection, but it is still an open question how close a functional equivalence is needed in order to capture life. Close enough to fool the Blind Watchmaker is probably close enough, but would that require molecular indistinguishability, and if so, do we really need to go that far?
  Additional links for this entry:
  http://eprints.resist.ecs.soton.ac.uk/3363/
  http://portal.acm.org/citation.cfm?id=188445
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/91/
  http://mitpress.mit.edu/catalog/item/default.asp?ttype=6&tid=40
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad94.artlife2.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad94.artlife2.html
  http://eprints.ecs.soton.ac.uk/archive/00003363/02/harnad94.artlife2.html
  http://cogprints.ecs.soton.ac.uk/archive/00001591/00/harnad94.artlife2.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1591
  
• Stevan Harnad (1991). Other bodies, other minds: A machine incarnation of an old philosophical problem.   (Abstract & more)
  Abstract: Explaining the mind by building machines with minds runs into the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is "everything" a body with a mind can do? Turing's original "pen-pal" version (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/archive/00003379/
  http://cogprints.ecs.soton.ac.uk/archive/00001578/
  http://portal.acm.org/citation.cfm?id=103493.103530
  http://cogprints.org/1578/0/harnad91.otherminds.html
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/78/
  http://eprints.ecs.soton.ac.uk/archive/00003379/01/harnad91.otherminds.html
  http://cogprints.ecs.soton.ac.uk/archive/00001578/00/harnad91.otherminds.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:3379
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1578
  http://www.springerlink.com/content/t5733025r3t44248/fulltext.pdf
  http://www.springerlink.com/index/T5733025R3T44248.pdf
  http://cogprints.org/1578/1/harnad91.otherminds.html
  
• Stevan Harnad (2006). The annotation game: On Turing (1950) on computing, machinery, and intelligence.   (Abstract & more)
  Abstract: This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/12954/
  http://eprints.resist.ecs.soton.ac.uk/7741/
  http://cogprints.ecs.soton.ac.uk/archive/00003322/
  http://www.ecs.soton.ac.uk/~harnad/Temp/turing.doc
  http://www.ecs.soton.ac.uk/~harnad/Temp/turing.html
  http://eprints.ecs.soton.ac.uk/archive/00007741/01/turing.html
  http://cogprints.ecs.soton.ac.uk/archive/00003322/01/turing.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:7741
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:eprints.ecs.soton.ac.uk:7741
  http://cogprints.org/3322/2/turing.pdf
  http://cogprints.org/3322/1/turing.html
  
• Stevan Harnad (2006). The annotation game: On Turing (1950) on computing, machinery, and intelligence.   (Abstract & more)
  Abstract: This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/12954/
  http://cogprints.org/3322/1/turing.html
  http://eprints.resist.ecs.soton.ac.uk/7741/
  http://cogprints.ecs.soton.ac.uk/archive/00003322/
  http://www.ecs.soton.ac.uk/~harnad/Temp/turing.doc
  http://www.ecs.soton.ac.uk/~harnad/Temp/turing.html
  http://eprints.ecs.soton.ac.uk/archive/00007741/01/turing.html
  http://cogprints.ecs.soton.ac.uk/archive/00003322/01/turing.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:7741
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:eprints.ecs.soton.ac.uk:7741
  http://cogprints.org/3322/2/turing.pdf
  
• Stevan Harnad (1992). The Turing test is not a trick: Turing indistinguishability is a scientific criterion.   (Abstract & more)
  Abstract: It is important to understand that the Turing Test (TT) is not, nor was it intended to be, a trick; how well one can fool someone is not a measure of scientific progress. The TT is an empirical criterion: It sets AI's empirical goal to be to generate human-scale performance capacity. This goal will be met when the candidate's performance is totally indistinguishable from a human's. Until then, the TT simply represents what it is that AI must endeavor eventually to accomplish scientifically
  Additional links for this entry:
  http://eprints.resist.ecs.soton.ac.uk/3373/
  http://portal.acm.org/citation.cfm?id=141420.141422
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/84/
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad92.turing.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:1584
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1584
  http://cogprints.org/1584/1/harnad92.turing.html
  http://cogprints.org/1584/0/harnad92.turing.html
  
• Larry Hauser (2001). Look who's moving the goal posts now.   (Abstract & more)
  Abstract:   The abject failure of Turing's first prediction (of computer success in playing the Imitation Game) confirms the aptness of the Imitation Game test as a test of human level intelligence. It especially belies fears that the test is too easy. At the same time, this failure disconfirms expectations that human level artificial intelligence will be forthcoming any time soon. On the other hand, the success of Turing's second prediction (that acknowledgment of computer thought processes would become commonplace) in practice amply confirms the thought that computers think in some manner and are possessed of some level of intelligence already. This lends ever-growing support to the hypothesis that computers will think at a human level eventually, despite the abject failure of Turing's first prediction
  Additional links for this entry:
  http://www.springerlink.com/content/p14045502315u372/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319542&PDF=1
  http://www.springerlink.com/index/P14045502315U372.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000001/00319542
  
• Larry Hauser (1993). Reaping the whirlwind: Reply to Harnad's Other Bodies, Other Minds.   (Abstract & more)
  Abstract:   Harnad''s proposed robotic upgrade of Turing''s Test (TT), from a test of linguistic capacity alone to a Total Turing Test (TTT) of linguisticand sensorimotor capacity, conflicts with his claim that no behavioral test provides even probable warrant for attributions of thought because there is no evidence of consciousness besides private experience. Intuitive, scientific, and philosophical considerations Harnad offers in favor of his proposed upgrade are unconvincing. I agree with Harnad that distinguishing real from as if thought on the basis of (presence or lack of) consciousness (thus rejecting Turing (behavioral) testing as sufficient warrant for mental attribution)has the skeptical consequence Harnad accepts — there is in factno evidence for me that anyone else but me has a mind. I disagree with hisacceptance of it! It would be better to give up the neo-Cartesian faith in private conscious experience underlying Harnad''s allegiance to Searle''s controversial Chinese Room Experiment than give up all claim to know others think. It would be better to allow that (passing) Turing''s Test evidences — evenstrongly evidences — thought
  Additional links for this entry:
  http://members.aol.com/lshauser/harnad3.html
  http://cogprints.ecs.soton.ac.uk/archive/00000241/00/reaping.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:241
  http://www.springerlink.com/content/h727v843532tp041/fulltext.pdf
  http://www.springerlink.com/index/H727V843532TP041.pdf
  
• Jose Hernandez-Orallo (2000). Beyond the Turing test.   (Abstract & more)
  Abstract: The main factor of intelligence is defined as the ability tocomprehend, formalising this ability with the help of new constructsbased on descriptional complexity. The result is a comprehension test,or C-test, which is exclusively defined in computational terms. Due toits absolute and non-anthropomorphic character, it is equally applicableto both humans and non-humans. Moreover, it correlates with classicalpsychometric tests, thus establishing the first firm connection betweeninformation theoretical notions and traditional IQ tests. The TuringTest is compared with the C-test and the combination of the two isquestioned. In consequence, the idea of using the Turing Test as apractical test of intelligence should be surpassed, and substituted bycomputational and factorial tests of different cognitive abilities, amuch more useful approach for artificial intelligence progress and formany other intriguing questions that present themselves beyond theTuring Test
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=595843.595997
  http://www.dsic.upv.es/~jorallo/escrits/TT-JHdez.ps.gz
  http://citeseer.ist.psu.edu/hernandez-orallo99beyond.html
  http://www.springerlink.com/content/h271n6173ll3773x/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=269489&PDF=1
  http://www.springerlink.com/index/H271N6173LL3773X.pdf
  http://www.ingentaconnect.com/content/klu/jlli/2000/00000009/00000004/00269489
  
• Addssdf Klsadlkjfs (ms). mind thing.   (More)
• Justin Leiber (2001). Turing and the fragility and insubstantiality of evolutionary explanations: A puzzle about the unity of Alan Turing's work with some larger implications.   (Abstract & more)
  Abstract: As is well known, Alan Turing drew a line, embodied in the "Turing test," between intellectual and physical abilities, and hence between cognitive and natural sciences. Less familiarly, he proposed that one way to produce a "passer" would be to educate a "child machine," equating the experimenter's improvements in the initial structure of the child machine with genetic mutations, while supposing that the experimenter might achieve improvements more expeditiously than natural selection. On the other hand, in his foundational "On the chemical basis of morphogenesis," Turing insisted that biological explanation clearly confine itself to purely physical and chemical means, eschewing vitalist and teleological talk entirely and hewing to D'Arcy Thompson's line that "evolutionary 'explanations,'" are historical and narrative in character, employing the same intentional and teleological vocabulary we use in doing human history, and hence, while perhaps on occasion of heuristic value, are not part of biology as a natural science. To apply Turing's program to recent issues, the attempt to give foundations to the social and cognitive sciences in the "real science" of evolutionary biology (as opposed to Turing's biology) is neither to give foundations, nor to achieve the unification of the social/cognitive sciences and the natural sciences
  Additional links for this entry:
  http://www.informaworld.com/smpp/./ftinterface~content=a713690490~fulltext=713240930
  
• Justin Leiber (2006). Turing's golden: How well Turing's work stands today.   (Abstract & more)
  Abstract: A. M. Turing has bequeathed us a conceptulary including 'Turing, or Turing-Church, thesis', 'Turing machine', 'universal Turing machine', 'Turing test' and 'Turing structures', plus other unnamed achievements. These include a proof that any formal language adequate to express arithmetic contains undecidable formulas, as well as achievements in computer science, artificial intelligence, mathematics, biology, and cognitive science. Here it is argued that these achievements hang together and have prospered well in the 50 years since Turing's death
  Additional links for this entry:
  http://www.informaworld.com/smpp/./ftinterface~content=a741477282~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/NGH381WR75272U7R.pdf
  http://www.informaworld.com/index/741477282.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2006/00000019/00000001/art00003
  http://www.ingentaconnect.com/content/routledg/cphp/latest
  
• Robert S. Lockhart (2000). Modularity, cognitive penetrability and the Turing test.   (Abstract & more)
  Abstract: The Turing Test blurs the distinction between a model and irrelevant) instantiation details. Modeling only functional modules is problematic if these are interconnected and cognitively penetrable
  Additional links for this entry:
  http://psycprints.ecs.soton.ac.uk/archive/00000068/
  
• Donald Michie (1993). Turing's test and conscious thought.   (More)
• P. Millar (1973). On the point of the imitation game.   (More)
  Additional links for this entry:
  http://links.jstor.org/sici?sici=0026-4423(197310)2:82:328<595:OTPOTI>2.0.CO;2-7
  http://www.jstor.org/sici?sici=0026-4423(197310)2:82:328<595:OTPOTI>2.0.CO;2-7
  http://www.jstor.org/stable/pdfplus/2252214.pdf
  
• James H. Moor (1978). Explaining computer behavior.   (More)
  Additional links for this entry:
  http://www.springerlink.com/content/x313g334823725mv/fulltext.pdf
  http://www.springerlink.com/index/X313G334823725MV.pdf
  
• James H. Moor (2001). The status and future of the Turing test.   (Abstract & more)
  Abstract:   The standard interpretation of the imitation game is defended over the rival gender interpretation though it is noted that Turing himself proposed several variations of his imitation game. The Turing test is then justified as an inductive test not as an operational definition as commonly suggested. Turing's famous prediction about his test being passed at the 70% level is disconfirmed by the results of the Loebner 2000 contest and the absence of any serious Turing test competitors from AI on the horizon. But, reports of the death of the Turing test and AI are premature. AI continues to flourish and the test continues to play an important philosophical role in AI. Intelligence attribution, methodological, and visionary arguments are given in defense of a continuing role for the Turing test. With regard to Turing's predictions one is disconfirmed, one is confirmed, but another is still outstanding
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA3-PA197&ots=blQIDolec0&sig=7Zb746qi1B6hBAlr4ehD3tEmgZc
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=RA3-PA197&ots=rzjaULCgnv&sig=BtFI0p2CUYyifPgOjuKkYI__bFc
  http://www.springerlink.com/content/ju585836x7571202/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319545&PDF=1
  http://www.springerlink.com/index/JU585836X7571202.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000001/00319545
  
• Shaun Nichols & Stephen P. Stich (1994). Folk psychology.   (Abstract & more)
  Abstract: For the last 25 years discussions and debates about commonsense psychology (or “folk psychology,” as it is often called) have been center stage in the philosophy of mind. There have been heated disagreements both about what folk psychology is and about how it is related to the scientific understanding of the mind/brain that is emerging in psychology and the neurosciences. In this chapter we will begin by explaining why folk psychology plays such an important role in the philosophy of mind. Doing that will require a quick look at a bit of the history of philosophical discussions about the mind. We’ll then turn our attention to the lively contemporary discussions aimed at clarifying the philosophical role that folk psychology is expected to play and at using findings in the cognitive sciences to get a clearer understanding of the exact nature of folk psychology
  Additional links for this entry:
  http://www.blutner.de/philom/Texte/folkpsych5.pdf
  http://www.blutner.de/NeuralNets/Texts/folkpsych5.pdf
  http://amor.rz.hu-berlin.de/~h0998dgh/philos/Texte/folkpsych5.pdf
  http://amor.cms.hu-berlin.de/~h0998dgh/philos/Texte/folkpsych5.pdf
  http://dingo.sbs.arizona.edu/~snichols/Papers/FolkPsychologyECS.pdf
  http://amor.rz.hu-berlin.de/~h0998dgh/NeuralNets/Texts/folkpsych5.pdf
  http://www.rci.rutgers.edu/~stich/Publications/Papers/FolkPsychology.pdf
  http://www.rci.rutgers.edu/~stich/Publications/Papers/FolkPsychology.pdf
  http://www-personal.umich.edu/~lormand/phil/teach/mmm/readings/Stich - Cognitive Science and the Appeal to Tacit Theory.rtf
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=8039374&dopt=Citation
  
• Gualtiero Piccinini (2000). Turing's rules for the imitation game.   (Abstract & more)
  Abstract:   In the 1950s, Alan Turing proposed his influential test for machine intelligence, which involved a teletyped dialogue between a human player, a machine, and an interrogator. Two readings of Turing''s rules for the test have been given. According to the standard reading of Turing''s words, the goal of the interrogator was to discover which was the human being and which was the machine, while the goal of the machine was to be indistinguishable from a human being. According to the literal reading, the goal of the machine was to simulate a man imitating a woman, while the interrogator – unaware of the real purpose of the test – was attempting to determine which of the two contestants was the woman and which was the man. The present work offers a study of Turing''s rules for the test in the context of his advocated purpose and his other texts. The conclusion is that there are several independent and mutually reinforcing lines of evidence that support the standard reading, while fitting the literal reading in Turing''s work faces severe interpretative difficulties. So, the controversy over Turing''s rules should be settled in favor of the standard reading
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=RA1-PA573&ots=rzj9ZTxlot&sig=sOABQfQAHOiAecLsjc4Dkdn0Kko
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA1-PA573&ots=blQHIwgjd_&sig=k_FvcCrJGK1pHaGSeZX1ciLb8SM
  http://www.springerlink.com/content/content/v1k277193084750u/fulltext.pdf
  http://www.springerlink.com/content/v1k277193084750u/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319535&PDF=1
  http://www.springerlink.com/index/V1K277193084750U.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000004/00319535
  
• William J. Rapaport (2000). How to pass a Turing test: Syntactic semantics, natural-language understanding, and first-person cognition.   (More)
  Additional links for this entry:
  http://www.cse.buffalo.edu/tech-reports/99-06.ps
  http://www.cse.buffalo.edu/tech-reports/99-06.ps.Z
  http://www.cse.buffalo.edu/~rapaport/Papers/turing.pdf
  http://historical.ncstrl.org/litesite-data/suny_buffalo_cs/99-06.ps
  http://www.kluweronline.com/article.asp?PIPS=269490&PDF=1
  http://www.springerlink.com/index/JW2371N7T37H2V10.pdf
  http://www.ingentaconnect.com/content/klu/jlli/2000/00000009/00000004/00269490
  
• William J. Rapaport (online). Review of The Turing Test: Verbal Behavior As the Hallmark of Intelligence.   (Abstract & more)
  Abstract: Stuart M. Shieber’s name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM (Shieber 1994a, 1994b; Loebner 1994).¹ With this collection, I expect it to become equally well known to philosophers
  Additional links for this entry:
  http://www.mitpressjournals.org/doi/pdf/10.1162/089120105774321127
  http://www.cse.buffalo.edu/~rapaport/Papers/shieberreview-original.pdf
  
• Ian Ravenscroft (online). Folk psychology as a theory.   (Abstract & more)
  Abstract: Many philosophers and cognitive scientists claim that our everyday or "folk" understanding of mental states constitutes a theory of mind. That theory is widely called "folk psychology" (sometimes "commonsense" psychology). The terms in which folk psychology is couched are the familiar ones of "belief" and "desire", "hunger", "pain" and so forth. According to many theorists, folk psychology plays a central role in our capacity to predict and explain the behavior of ourselves and others. However, the nature and status of folk psychology remains controversial
  Additional links for this entry:
  http://plato.stanford.edu/entries/folkpsych-theory
  http://plato.stanford.edu/entries/folkpsych-theory/
  http://www.seop.leeds.ac.uk/entries/folkpsych-theory/
  http://www.illc.uva.nl/~seop/entries/folkpsych-theory/
  http://www.science.uva.nl/~seop/entries/folkpsych-theory/
  http://setis.library.usyd.edu.au/stanford/entries/folkpsych-theory/
  
• Kris Rhodes (ms). Vindication of the Rights of Machine.   (Abstract & more)
  Abstract: In this paper, I argue that certain Machines can have rights independently of whether they are sentient, or conscious, or whatever you might call it.
  Additional links for this entry:
  http://www.igradeyourpaper.com/VRMv3.doc
  
• Geoffrey Sampson (1973). In defence of Turing.   (More)
  Additional links for this entry:
  http://links.jstor.org/sici?sici=0026-4423(197310)2:82:328<592:IDOT>2.0.CO;2-M
  http://www.jstor.org/sici?sici=0026-4423(197310)2:82:328<592:IDOT>2.0.CO;2-M
  http://www.jstor.org/stable/pdfplus/2252213.pdf
  
• Y. Sato & T. Ikegami (2004). Undecidability in the imitation game.   (Abstract & more)
  Abstract:   This paper considers undecidability in the imitation game, the so-called Turing Test. In the Turing Test, a human, a machine, and an interrogator are the players of the game. In our model of the Turing Test, the machine and the interrogator are formalized as Turing machines, allowing us to derive several impossibility results concerning the capabilities of the interrogator. The key issue is that the validity of the Turing test is not attributed to the capability of human or machine, but rather to the capability of the interrogator. In particular, it is shown that no Turing machine can be a perfect interrogator. We also discuss meta-imitation game and imitation game with analog interfaces where both the imitator and the interrogator are mimicked by continuous dynamical systems
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=976736
  http://www.bdc.brain.riken.jp/publications/internal/jour-Undecidability_in_the_Imitation_Game/uig.pdf
  http://www.bdc.brain.riken.go.jp/publications/internal/jour-Undecidability_in_the_Imitation_Game/uig.pdf
  http://www.bdc.brain.riken.go.jp/publications/internal/trep-Undecidability_in_the_imitation_game/riken-bsi-bdc-tr2001-04.ps.Z
  http://www.springerlink.com/content/t2771g8512m67737/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5253133&PDF=1
  http://www.springerlink.com/index/T2771G8512M67737.pdf
  http://www.ingentaconnect.com/content/klu/mind/2004/00000014/00000002/05253133
  
• Ayse P. Saygin, Ilyas Cicekli & Varol Akman (2000). Turing test: 50 years later.   (Abstract & more)
  Abstract:   The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing''s ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the `other minds'' problem, and similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic
  Additional links for this entry:
  http://citeseer.ist.psu.edu/378189.html
  http://crl.ucsd.edu/~saygin/papers/MMTT.pdf
  http://portal.acm.org/citation.cfm?id=596724.596952
  http://cogprints.ecs.soton.ac.uk/archive/00001925/00/tt50.ps
  http://www.cs.bilkent.edu.tr/~akman/jour-papers/mam/mam2000.pdf
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1925
  http://www.springerlink.com/content/content/ph7275k8w0137245/fulltext.pdf
  http://www.springerlink.com/content/ph7275k8w0137245/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319526&PDF=1
  http://www.springerlink.com/index/PH7275K8W0137245.pdf
  http://cogprints.org/1925/2/tt50.ps
  http://cogprints.org/1925/0/tt50.ps
  
• A. P. Saygin & I. Cicekli (2000). Turing test: 50 years later.   (Abstract & more)
  Abstract: The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philo- sophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing’s ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the ‘other minds’ problem, and similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic
  Additional links for this entry:
  http://citeseer.ist.psu.edu/378189.html
  http://crl.ucsd.edu/~saygin/papers/MMTT.pdf
  http://portal.acm.org/citation.cfm?id=596724.596952
  http://cogprints.ecs.soton.ac.uk/archive/00001925/00/tt50.ps
  http://www.cs.bilkent.edu.tr/~akman/jour-papers/mam/mam2000.pdf
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1925
  http://www.kluweronline.com/article.asp?PIPS=319526&PDF=1
  http://www.springerlink.com/index/PH7275K8W0137245.pdf
  http://cogprints.org/1925/2/tt50.ps
  http://cogprints.org/1925/0/tt50.ps
  
• Paul Schweizer (1998). The truly total Turing test.   (Abstract & more)
  Abstract:   The paper examines the nature of the behavioral evidence underlying attributions of intelligence in the case of human beings, and how this might be extended to other kinds of cognitive system, in the spirit of the original Turing Test (TT). I consider Harnad's Total Turing Test (TTT), which involves successful performance of both linguistic and robotic behavior, and which is often thought to incorporate the very same range of empirical data that is available in the human case. However, I argue that the TTT is still too weak, because it only tests the capabilities of particular tokens within a preexisting context of intelligent behavior. What is needed is a test of the cognitive type, as manifested through a number of exemplary tokens, in order to confirm that the cognitive type is able to produce the context of intelligent behavior presupposed by tests such as the TT and TTT
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=371134CI
  http://www.springerlink.com/content/h51pp233m1h732p0/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=150465&PDF=1
  http://www.springerlink.com/index/H51PP233M1H732P0.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000008/00000002/00150465
  
• James F. Sennett (ms). The ice man cometh: Lt. comander data and the Turing test.   (More)
• Stuart M. Shieber (1994). Lessons from a restricted Turing test.   (More)
  Additional links for this entry:
  http://adsabs.harvard.edu/abs/1994cmp.lg....4002S
  http://citeseer.ist.psu.edu/shieber93lessons.html
  http://www.eecs.harvard.edu/shieber/Biblio/Papers/loebner-rev-html/loebner-rev-html.html
  http://www.eecs.harvard.edu/~shieber/Biblio/Papers/loebner-rev-html/loebner-rev-html.html
  http://portal.acm.org/citation.cfm?id=175208.175217&dl=GUIDE&dl=ACM&idx=175208&part=periodical&WantType=periodical&title=Communications of the ACM
  
• Susan G. Sterrett (2002). Nested algorithms and the original imitation game test: A reply to James Moor.   (More)
  Additional links for this entry:
  http://www.springerlink.com/content/x4225320762736q0/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=391369&PDF=1
  http://www.springerlink.com/index/X4225320762736Q0.pdf
  http://www.ingentaconnect.com/content/klu/mind/2002/00000012/00000001/00391369
  
• Susan G. Sterrett (2000). Turing's two tests for intelligence.   (Abstract & more)
  Abstract:   On a literal reading of `Computing Machinery and Intelligence'', Alan Turing presented not one, but two, practical tests to replace the question `Can machines think?'' He presented them as equivalent. I show here that the first test described in that much-discussed paper is in fact not equivalent to the second one, which has since become known as `the Turing Test''. The two tests can yield different results; it is the first, neglected test that provides the more appropriate indication of intelligence. This is because the features of intelligence upon which it relies are resourcefulness and a critical attitude to one''s habitual responses; thus the test''s applicablity is not restricted to any particular species, nor does it presume any particular capacities. This is more appropriate because the question under consideration is what would count as machine intelligence. The first test realizes a possibility that philosophers have overlooked: a test that uses a human''s linguistic performance in setting an empirical test of intelligence, but does not make behavioral similarity to that performance the criterion of intelligence. Consequently, the first test is immune to many of the philosophical criticisms on the basis of which the (so-called) `Turing Test'' has been dismissed
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA1-PA519&ots=blQIDonh93&sig=kpE5iqY7Fjm67XjSS7t2e9XqGDo
  http://www.springerlink.com/content/content/k11t54j7821h4230/fulltext.pdf
  http://www.springerlink.com/content/k11t54j7821h4230/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319533&PDF=1
  http://www.springerlink.com/index/K11T54J7821H4230.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000004/00319533
  
• Cristi Stoica, Turing test, easy to pass; human mind, hard to understand.   (Abstract & more)
  Abstract: Under general assumptions, the Turing test can be easily passed by an appropriate algorithm. I show that for any test satisfying several general conditions, we can construct an algorithm that can pass that test, hence, any operational definition is easy to fulfill. I suggest a test complementary to Turing's test, which will measure our understanding of the human mind. The Turing test is required to fix the operational specifications of the algorithm under test; under this constrain, the additional test simply consists in measuring the length of the algorithm
• Saul Traiger (2000). Making the right identification in the Turing test.   (Abstract & more)
  Abstract:   The test Turing proposed for machine intelligence is usually understood to be a test of whether a computer can fool a human into thinking that the computer is a human. This standard interpretation is rejected in favor of a test based on the Imitation Game introduced by Turing at the beginning of "Computing Machinery and Intelligence."
  Additional links for this entry:
  http://www.traiger.oxy.edu/publications/making-the-right-identification.pdf
  http://faculty.oxy.edu/traiger/publications/making-the-right-identification.pdf
  http://0-www.faculty.oxy.edu.oasys.lib.oxy.edu/traiger/publications/making-the-right-identification.pdf
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=RA1-PA561&ots=rzjaULFcit&sig=SFxczaJwBF-Ba70js7yMbXB8L6Y
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA1-PA561&ots=blQIDooa7_&sig=4ZGzMTB0yiYy62v_qt0Q_DwtyKI
  http://www.springerlink.com/content/g90h6kqu506lwr75/fulltext.pdf
  http://www.springerlink.com/index/G90H6KQU506LWR75.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000004/00319538
  
• Peter Turney (ms). Answering subcognitive Turing test questions: A reply to French.   (Abstract & more)
  Abstract: Robert French has argued that a disembodied computer is incapable of passing a Turing Test that includes subcognitive questions. Subcognitive questions are designed to probe the network of cultural and perceptual associations that humans naturally develop as we live, embodied and embedded in the world. In this paper, I show how it is possible for a disembodied computer to answer subcognitive questions appropriately, contrary to French’s claim. My approach to answering subcognitive questions is to use statistical information extracted from a very large collection of text. In particular, I show how it is possible to answer a sample of subcognitive questions taken from French, by issuing queries to a search engine that indexes about 350 million Web pages. This simple algorithm may shed light on the nature of human (sub-) cognition, but the scope of this paper is limited to demonstrating that French is mistaken: a disembodied computer can answer subcognitive questions
  Additional links for this entry:
  http://adsabs.harvard.edu/abs/2002cs.......12015T
  http://citeseer.ist.psu.edu/turney01answering.html
  http://cogprints.ecs.soton.ac.uk/archive/00001798/
  http://www.extractor.com/download/documents/subcognitive.pdf
  http://cogprints.ecs.soton.ac.uk/archive/00001798/00/subcognitive.pdf
  https://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-44898.pdf
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1798
  http://taylorandfrancis.metapress.com/index/TW3Q03K8NK7UVQJG.pdf
  http://www.informaworld.com/index/TW3Q03K8NK7UVQJG.pdf
  http://www.ingentaconnect.com/content/tandf/teta/2001/00000013/00000004/art00006
  http://cogprints.org/1798/1/subcognitive.ps
  http://cogprints.org/1798/0/subcognitive.pdf
  
• Alan M. Turing (1950). Computing machinery and intelligence.   (Abstract & more)
  Abstract: I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
  Additional links for this entry:
  http://www.ttt.org/ling490/TuringTest.pdf
  http://www.agent.ai/doc/upload/200302/turi50_1.pdf
  http://eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ216711
  http://www.dcs.gla.ac.uk/~joe/Teaching/ATiCS/FC-TuringAI-intro.pdf
  http://www.dfki.uni-sb.de/imedia/lidos/bibtex/Collins_a10168-96.html
  http://www.marxists.org/reference/subject/philosophy/works/en/turing.htm
  http://lia.deis.unibo.it/corsi/2005-2006/SID-LS-CE/downloads/turing-article.pdf
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:499
  http://twiki.di.uniroma1.it/twiki/viewfile/Users/MarcoBianchi?rev=1.1;filename=TuringComputingMachineryAndIntelligence.pdf
  http://archive.computerhistory.org/projects/chess/related_materials/text/2-0 and 2-1.Computing_machinery_and_intelligence.turing/2-0 and 2-1.Computing_machinery_and_intelligence.turing-alan.mind-59.1950.062303001.pdf
  http://links.jstor.org/sici?sici=0026-4423(195010)2:59:236<433:CMAI>2.0.CO;2-5
  http://www.jstor.org/sici?sici=0026-4423(195010)2:59:236<433:CMAI>2.0.CO;2-5
  http://mind.oxfordjournals.org/cgi/reprint/LIX/236/433
  http://www.jstor.org/stable/pdfplus/2251299.pdf
  http://cogprints.org/499/1/turing.html
  http://cogprints.org/499/0/turing.html
  
• S. Watt (1996). Naive psychology and the inverted Turing test.   (Abstract & more)
  Abstract: This target article argues that the Turing test implicitly rests on a "naive psychology," a naturally evolved psychological faculty which is used to predict and understand the behaviour of others in complex societies. This natural faculty is an important and implicit bias in the observer's tendency to ascribe mentality to the system in the test. The paper analyses the effects of this naive psychology on the Turing test, both from the side of the system and the side of the observer, and then proposes and justifies an inverted version of the test which allows the processes of ascription to be analysed more directly than in the standard version
  Additional links for this entry:
  http://citeseer.ist.psu.edu/watt96naive.html
  http://psycprints.ecs.soton.ac.uk/archive/00000506/
  http://psycprints.ecs.soton.ac.uk/perl/local/psyc/makedoc?id=506&type=html
  http://psycprints.ecs.soton.ac.uk/archive/00000506/02/psyc.96.7.14.turing-test.1.watt
  http://psycprints.ecs.soton.ac.uk/archive/00000506/01/psyc.96.7.14.turing-test.1.watt.xml
  
• Blay Whitby (1996). Why the Turing test is ai's biggest blind Alley.   (More)
• Sean Zdenek (2001). Passing loebner's Turing test: A case of conflicting discourse functions.   (Abstract & more)
  Abstract:   This paper argues that the Turing test is based on a fixed and de-contextualized view of communicative competence. According to this view, a machine that passes the test will be able to communicate effectively in a variety of other situations. But the de-contextualized view ignores the relationship between language and social context, or, to put it another way, the extent to which speakers respond dynamically to variations in discourse function, formality level, social distance/solidarity among participants, and participants' relative degrees of power and status (Holmes, 1992). In the case of the Loebner Contest, a present day version of the Turing test, the social context of interaction can be interpreted in conflicting ways. For example, Loebner discourse is defined 1) as a friendly, casual conversation between two strangers of equal power, and 2) as a one-way transaction in which judges control the conversational floor in an attempt to expose contestants that are not human. This conflict in discourse function is irrelevant so long as the goal of the contest is to ensure that only thinking, human entities pass the test. But if the function of Loebner discourse is to encourage the production of software that can pass for human on the level of conversational ability, then the contest designers need to resolve this ambiguity in discourse function, and thus also come to terms with the kind of competence they are trying to measure
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=RA2-PA53&ots=rzjaULFlks&sig=a-VgtvUjcFyxiSpkIASbwHF4Imk
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA1-PA583&ots=blQIDooj9Z&sig=IxxOEy4OGv0MfwNy7oTRa0j0_9g
  http://www.springerlink.com/content/p1572g5485036674/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=319544&PDF=1
  http://www.springerlink.com/index/P1572G5485036674.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000001/00319544
  

Godelian arguments

• Damjan Bojadziev (1997). Mind versus Godel.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=VHZy4j_Qa-cC&oi=fnd&pg=PA202&dq=+Mind+versus+Godel+Bojadziev&ots=RjBGkflpmk&sig=oyuz5heebVFZdtmfGXFv50bP2zo
  
• Selmer Bringsjord & H. Xiao (2000). A refutation of Penrose's new Godelian case against the computational conception of mind.   (More)
• David J. Chalmers (1996). Minds, machines, and mathematics.   (Abstract & more)
  Abstract: In his stimulating book SHADOWS OF THE MIND, Roger Penrose presents arguments, based on Gödel's theorem, for the conclusion that human thought is uncomputable. There are actually two separate arguments in Penrose's book. The second has been widely ignored, but seems to me to be much more interesting and novel than the first. I will address both forms of the argument in some detail. Toward the end, I will also comment on Penrose's proposals for a "new science of consciousness"
  Additional links for this entry:
  http://www.u.arizona.edu/~chalmers/papers/penrose.html
  http://psyche.cs.monash.edu.au/v2/psyche-2-09-chalmers.html
  http://calculemus.org/MathUniversalis/NS/10/03chalmers.html
  http://psyche.cs.monash.edu.au/volume2-1/psyche-95-2-09-shadows-7-chalmers.html
  
• Jack Copeland (1998). Turing's o-machines, Searle, Penrose, and the brain.   (Abstract & more)
  Abstract: In his PhD thesis (1938) Turing introduced what he described as 'a new kind of machine'. He called these 'O-machines'. The present paper employs Turing's concept against a number of currently fashionable positions in the philosophy of mind
  Additional links for this entry:
  http://www.alanturing.net/turing_archive/pages/pub/turing1/turing1.pdf
  http://www.blackwell-synergy.com/links/doi/10.1111/1467-8284.00113
  http://www.blackwell-synergy.com/doi/abs/10.1111/1467-8284.00113
  http://www.ingentaconnect.com/content/bpl/anal/1998/00000058/00000258/art00113
  
• Daniel C. Dennett (1989). Murmurs in the cathedral: Review of R. Penrose, The Emperor's New Mind.   (Abstract & more)
  Abstract: The idea that a computer could be conscious--or equivalently, that human consciousness is the effect of some complex computation mechanically performed by our brains--strikes some scientists and philosophers as a beautiful idea. They find it initially surprising and unsettling, as all beautiful ideas are, but the inevitable culmination of the scientific advances that have gradually demystified and unified the material world. The ideologues of Artificial Intelligence (AI) have been its most articulate supporters. To others, this idea is deeply repellent: philistine, reductionistic (in some bad sense), as incredible as it is offensive. John Searle's attack on "strong AI" is the best known expression of this view, but others in the same camp, liking Searle's destination better than his route, would dearly love to see a principled, scientific argument showing that strong AI is impossible. Roger Penrose has set out to provide just such an argument
• S. Feferman (1996). Penrose's Godelian argument.   (Abstract & more)
  Abstract: In his book Shadows of the Mind: A search for the missing science of con- sciousness [SM below], Roger Penrose has turned in another bravura perfor- mance, the kind we have come to expect ever since The Emperor’s New Mind [ENM ] appeared. In the service of advancing his deep convictions and daring conjectures about the nature of human thought and consciousness, Penrose has once more drawn a wide swath through such topics as logic, computa- tion, artificial intelligence, quantum physics and the neuro-physiology of the brain, and has produced along the way many gems of exposition of difficult mathematical and scientific ideas, without condescension, yet which should be broadly appealing.¹ While the aims and a number of the topics in SM are the same as in ENM , the focus now is much more on the two axes that Pen- rose grinds in earnest. Namely, in the first part of SM he argues anew and at great length against computational models of the mind and more specifi- cally against any account of mathematical thought in computational terms. Then in the second part, he argues that there must be a scientific account of consciousness but that will require a (still to be found) non-computational extension or modification of present-day quantum physics
• H. Gaifman (2000). What Godel's incompleteness result does and does not show.   (Abstract & more)
  Abstract: In a recent paper S. McCall adds another link to a chain of attempts to enlist Gödel’s incompleteness result as an argument for the thesis that human reasoning cannot be construed as being carried out by a computer.1 McCall’s paper is undermined by a technical oversight. My concern however is not with the technical point. The argument from Gödel’s result to the no-computer thesis can be made without following McCall’s route; it is then straighter and more forceful. Yet the argument fails in an interesting and revealing way. And it leaves a remainder: if some computer does in fact simulate all our mathematical reasoning, then, in principle, we cannot fully grasp how it works. Gödel’s result also points out a certain essential limitation of self-reflection. The resulting picture parallels, not accidentally, Davidson’s view of psychology, as a science that in principle must remain “imprecise”, not fully spelt out. What is intended here by “fully grasp”, and how all this is related to self-reflection, will become clear at the end of this comment
  Additional links for this entry:
  http://links.jstor.org/sici?sici=0022-362X(200008)97:8<462:WGIRDA>2.0.CO;2-N
  http://www.jstor.org/sici?sici=0022-362X(200008)97:8<462:WGIRDA>2.0.CO;2-N
  http://www.jstor.org/stable/pdfplus/2678427.pdf
  
• Robert M. Gordon (online). Folk Psychology As Mental Simulation.   (Abstract & more)
  Abstract: by, or is otherwise relevant to the seminar "Folk Psychology vs. Mental Simulation: How Minds Understand Minds," a National
• Rick Grush & P. Churchland (1995). Gaps in Penrose's toiling.   (Abstract & more)
  Abstract: Using the Gödel Incompleteness Result for leverage, Roger Penrose has argued that the mechanism for consciousness involves quantum gravitational phenomena, acting through microtubules in neurons. We show that this hypothesis is implausible. First, the Gödel Result does not imply that human thought is in fact non algorithmic. Second, whether or not non algorithmic quantum gravitational phenomena actually exist, and if they did how that could conceivably implicate microtubules, and if microtubules were involved, how that could conceivably implicate consciousness, is entirely speculative. Third, cytoplasmic ions such as calcium and sodium are almost certainly present in the microtubule pore, barring the quantum mechanical effects Penrose envisages. Finally, physiological evidence indicates that consciousness does not directly depend on microtubule properties in any case, rendering doubtful any theory according to which consciousness is generated in the microtubules
  Additional links for this entry:
  http://mind.ucsd.edu/papers/penrose/penrose.rtf
  
• Jeffrey Ketland & Panu Raatikainen (online). Truth and provability again.   (More)
• Geoffrey Laforte, Pat Hayes & Kenneth M. Ford (1998). Why Godel's theorem cannot refute computationalism: A reply to Penrose.   (More)
• Alan M. Leslie, Shaun Nichols, Stephen P. Stich & David B. Klein (1996). Varieties of off-line simulation.   (Abstract & more)
  Abstract: In the last few years, off-line simulation has become an increasingly important alternative to standard explanations in cognitive science. The contemporary debate began with Gordon (1986) and Goldman's (1989) off-line simulation account of our capacity to predict behavior. On their view, in predicting people's behavior we take our own decision making system `off line' and supply it with the `pretend' beliefs and desires of the person whose behavior we are trying to predict; we then let the decision maker reach a decision on the basis of these pretend inputs. Figure 1 offers a `boxological' version of the off-line simulation theory of behavior prediction.(1)
• John R. Lucas (1967). Human and machine logic: A rejoinder.   (Abstract & more)
  Abstract: We can imagine a human operator playing a game of one-upmanship against a programmed computer. If the program is Fn, the human operator can print the theorem Gn, which the programmed computer, or, if you prefer, the program, would never print, if it is consistent. This is true for each whole number n, but the victory is a hollow one since a second computer, loaded with program C, could put the human operator out of a job.... It is useless for the `mentalist' to argue that any given program can always be improves since the process for improving programs can presumably be programmed also; certainly this can be done if the mentalist describes how the improvement is to be made. If he does give such a description, then he has not made a case
  Additional links for this entry:
  http://www.univ.trieste.it/~etica/2003_1/6_monographica.doc
  http://links.jstor.org/sici?sici=0007-0882(196808)19:2<155:HAMLAR>2.0.CO;2-4
  http://www.jstor.org/sici?sici=0007-0882(196808)19:2<155:HAMLAR>2.0.CO;2-4
  http://bjps.oxfordjournals.org/cgi/content/citation/19/2/155
  http://bjps.oxfordjournals.org/cgi/reprint/19/2/155
  http://www.jstor.org/stable/pdfplus/686794.pdf
  
• John R. Lucas (1970). Mechanism: A rejoinder.   (More)
• John R. Lucas (1961). Minds, machines and Godel.   (Abstract & more)
  Abstract: Goedel's theorem states that in any consistent system which is strong enough to produce simple arithmetic there are formulae which cannot be proved-in-the-system, but which we can see to be true. Essentially, we consider the formula which says, in effect, "This formula is unprovable-in-the-system". If this formula were provable-in-the-system, we should have a contradiction: for if it were provablein-the-system, then it would not be unprovable-in-the-system, so that "This formula is unprovable-in-the-system" would be false: equally, if it were provable-in-the-system, then it would not be false, but would be true, since in any consistent system nothing false can be provedin-the-system, but only truths. So the formula "This formula is unprovable-in-the-system" is not provable-in-the-system, but unprovablein-the-system. Further, if the formula "This formula is unprovablein- the-system" is unprovable-in-the-system, then it is true that that formula is unprovable-in-the-system, that is, "This formula is unprovable-in-the-system" is true. Goedel's theorem must apply to cybernetical machines, because it is of the essence of being a machine, that it should be a concrete instantiation of a formal system. It follows that given any machine which is consistent and capable of doing simple arithmetic, there is a formula which it is incapable of producing as being true---i.e., the formula is unprovable-in-the-system-but which we can see to be true. It follows that no machine can be a complete or adequate model of the mind, that minds are essentially different from machines
  Additional links for this entry:
  http://cogprints.org/356/1/lucas.html
  
• John R. Lucas (1968). Satan stultified: A rejoinder to Paul Benacerraf.   (Abstract & more)
  Abstract: The argument is a dialectical one. It is not a direct proof that the mind is something more than a machine, but a schema of disproof for any particular version of mechanism that may be put forward. If the mechanist maintains any specific thesis, I show that [146] a contradiction ensues. But only if. It depends on the mechanist making the first move and putting forward his claim for inspection. I do not think Benacerraf has quite taken the point. He criticizes me both for "failing to notice" that my ability to show that the Gödel sentence of a formal system is true "depends very much on how he is given
• John R. Lucas (ms). The Godelian argument: Turn over the page.   (Abstract & more)
  Abstract: I have no quarrel with the first two sentences: but the third, though charitable and courteous, is quite untrue. Although there are criticisms which can be levelled against the Gödelian argument, most of the critics have not read either of my, or either of Penrose's, expositions carefully, and seek to refute arguments we never put forward, or else propose as a fatal objection one that had already been considered and countered in our expositions of the argument. Hence my title. The Gödelian Argument uses Gödel's theorem to show that minds cannot be explained in purely mechanist terms. It has been put forward, in different forms, by Gödel himself, by Penrose, and by me
• John R. Lucas (ms). The implications of Godel's theorem.   (Abstract & more)
  Abstract: In 1931 Kurt Gödel proved two theorems about the completeness and consistency of first-order arithmetic. Their implications for philosophy are profound. Many fashionable tenets are shown to be untenable: many traditional intuitions are vindicated by incontrovertible arguments
  Additional links for this entry:
  http://users.ox.ac.uk/~jrlucas/Godel/goedhand.html
  http://users.ox.ac.uk/~jrlucas/Godel/implgoed.html
  
• Tim Maudlin (1996). Between the motion and the act.   (More)
  Additional links for this entry:
  http://psyche.cs.monash.edu.au/v2/psyche-2-02-maudlin.html
  http://psyche.cs.monash.edu.au/volume2-1/psyche-95-2-02-shadows-1-maudlin.html
  
• Storrs McCall (1999). Can a Turing machine know that the Godel sentence is true?   (More)
  Additional links for this entry:
  http://links.jstor.org/sici?sici=0022-362X(199910)96:10<525:CATMKT>2.0.CO;2-2
  http://www.jstor.org/sici?sici=0022-362X(199910)96:10<525:CATMKT>2.0.CO;2-2
  http://www.jstor.org/stable/pdfplus/2564612.pdf
  
• E. Nelson (2002). Mathematics and the mind.   (More)
  Additional links for this entry:
  http://www.math.princeton.edu/~nelson/papers/tokyo.pdf
  http://www.math.princeton.edu/~nelson/papers/tokyo.ps.gz
  http://star.tau.ac.il/~eshel/Bio_complexity/Conceptual Background/Mathematics-mind.pdf
  http://books.google.com/books?hl=en&lr=&id=eRkooap_j-YC&oi=fnd&pg=PA89&ots=1D3F8NBtaN&sig=L490G6v3TwiKps8crkNSHEMZak4
  
• Roger Penrose (1996). Beyond the doubting of a shadow.   (More)
  Additional links for this entry:
  http://calculemus.org/MathUniversalis/NS/10/01penrose.html
  http://psyche.csse.monash.edu.au/v2/psyche-2-23-penrose.html
  http://psyche.cs.monash.edu.au/volume2-1/psyche-96-2-23-shadows-10-penrose.html
  
• Gualtiero Piccinini (2003). Alan Turing and the mathematical objection.   (Abstract & more)
  Abstract: This paper concerns Alan Turing’s ideas about machines, mathematical methods of proof, and intelligence. By the late 1930s, Kurt Gödel and other logicians, including Turing himself, had shown that no finite set of rules could be used to generate all true mathematical statements. Yet according to Turing, there was no upper bound to the number of mathematical truths provable by intelligent human beings, for they could invent new rules and methods of proof. So, the output of a human mathematician, for Turing, was not a computable sequence (i.e., one that could be generated by a Turing machine). Since computers only contained a finite number of instructions (or programs), one might argue, they could not reproduce human intelligence. Turing called this the “mathematical
  objection” to his view that machines can think. Logico-mathematical reasons, stemming from his own work, helped to convince Turing that it should be possible to reproduce human intelligence, and eventually compete with it, by developing the appropriate kind of digital computer. He felt it
  should be possible to program a computer so that it could learn or discover new rules, overcoming the limitations imposed by the incompleteness and undecidability results in the same way that human
  mathematicians presumably do.
  Additional links for this entry:
  http://www.kluweronline.com/article.asp?PIPS=5098147&PDF=1
  http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000001/05098147
  http://www.springerlink.com/index/UL15V486260V8Q79.pdf
  http://www.umsl.edu/~piccininig/Alan_Turing_and_Mathematical_Objection.pdf
  
• Hilary Putnam (1985). Reflexive reflections.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=8AT_7ZEOFf4C&oi=fnd&pg=RA2-PA143&ots=2yuzpRMYBj&sig=UU2i1OXUpETqyryBJ8N290QLcJc
  http://www.springerlink.com/content/kr0702201813pk58/fulltext.pdf
  http://www.springerlink.com/index/KR0702201813PK58.pdf
  
• Panu Raatikainen, McCall's gödelian argument is invalid.   (Abstract & more)
  Abstract: Storrs McCall continues the tradition of Lucas and Penrose in an attempt to refute mechanism by appealing to Gödel’s incompleteness theorem (McCall 2001). That is, McCall argues that Gödel’s theorem “reveals a sharp dividing line between human and machine thinking”. According to McCall, “[h]uman beings are familiar with the distinction between truth and theoremhood, but Turing machines cannot look beyond their own output”. However, although McCall’s argumentation is slightly more sophisticated than the earlier Gödelian anti-mechanist arguments, in the end it fails badly, as it is at odds with the logical facts
• Panu Raatikainen (2005). On the philosophical relevance of gödel's incompleteness theorems.   (Abstract & more)
  Abstract: Gödel began his 1951 Gibbs Lecture by stating: “Research in the foundations of mathematics during the past few decades has produced some results which seem to me of interest, not only in themselves, but also with regard to their implications for the traditional philosophical problems about the nature of mathematics.” (Gödel 1951) Gödel is referring here especially to his own incompleteness theorems (Gödel 1931). Gödel’s first incompleteness theorem (as improved by Rosser (1936)) says that for any consistent formalized system F, which contains elementary arithmetic, there exists a sentence GF of the language of the system which is true but unprovable in that system. Gödel’s second incompleteness theorem states that no consistent formal system can prove its own consistency
• Panu Raatikainen (2005). Truth and provability: A comment on Redhead.   (Abstract & more)
  Abstract: Michael Redhead's recent argument aiming to show that humanly certifiable truth outruns provability is critically evaluated. It is argued that the argument is at odds with logical facts and fails
  Additional links for this entry:
  http://bjps.oxfordjournals.org/cgi/content/abstract/56/3/611
  http://bjps.oxfordjournals.org/cgi/rapidpdf/axi134v1.pdf
  http://bjps.oxfordjournals.org/cgi/reprint/56/3/611
  http://www.ingentaconnect.com/content/oup/phisci/2005/00000056/00000003/art00611
  
• Panu Raatikainen (ms). Truth and provability again.   (Abstract & more)
  Abstract: Lucas and Redhead ([2007]) announce that they will defend the views of Redhead ([2004]) against the argument by Panu Raatikainen ([2005]). They certainly re-state the main claims of Redhead ([2004]), but they do not give any real arguments in their favour, and do not provide anything that would save Redhead’s argument from the serious problems pointed out in (Raatikainen [2005]). Instead, Lucas and Redhead make a number of seemingly irrelevant points, perhaps indicating a failure to understand the logico-mathematical points at issue
• William E. Seager (2003). Yesterday's algorithm: Penrose and the Godel argument.   (Abstract & more)
  Abstract: Roger Penrose is justly famous for his work in physics and mathematics but he is _notorious_ for his endorsement of the Gödel argument (see his 1989, 1994, 1997). This argument, first advanced by J. R. Lucas (in 1961), attempts to show that Gödel’s (first) incompleteness theorem can be seen to reveal that the human mind transcends all algorithmic models of it¹. Penrose's version of the argument has been seen to fall victim to the original objections raised against Lucas (see Boolos (1990) and for a particularly intemperate review, Putnam (1994)). Yet I believe that more can and should be said about the argument. Only a brief review is necessary here although I wish to present the argument in a somewhat peculiar form
• Aaron Sloman (1986). The emperor's real mind.   (More)
• Tony Stone & Martin Davies (1998). Folk psychology and mental simulation.   (Abstract & more)
  Abstract: This paper is about the contemporary debate concerning folk psychology – the debate between the proponents of the theory theory of folk psychology and the friends of the simulation alternative.¹ At the outset, we need to ask: What should we mean by this term ‘folk psychology’?
  Additional links for this entry:
  http://philrsss.anu.edu.au/~mdavies/papers/simrip.pdf
  http://www.nyu.edu/gsas/dept/philo/courses/concepts/folkpsychology.html
  

The Chinese Room

• Kristin Andrews (online). On predicting behavior.   (Abstract & more)
  Abstract: I argue that the behavior of other agents is insufficiently described in current debates as a dichotomy between tacit theory (attributing beliefs and desires to predict behavior) and simulation theory (imagining what one would do in similar circumstances in order to predict behavior). I introduce two questions about the foundation and development of our ability both to attribute belief and to simulate it. I then propose that there is one additional method used to predict behavior, namely, an inductive strategy
• Jay David Atlas, What is it like to be a chinese room?   (Abstract & more)
  Abstract: When philosophers think about mental phenomena, they focus on several features of human experience: (1) the existence of consciousness, (2) the intentionality of mental states, that property by which beliefs, desires, anger, etc. are directed at, are about, or refer to objects and states of affairs, (3) subjectivity, characterized by my feeling my pains but not yours, by my experiencing the world and myself from my point of view and not yours, (4) mental causation, that thoughts and feelings have physical effects on the world: I decide to raise my arm and my arm rises. In a world described by theories of physics and chemistry, what place in that physical description do descriptions of the mental have?
  Additional links for this entry:
  http://pages.pomona.edu/~jda14747/Atlas_WHAT%20IS%20IT%20LIKE%20TO%20BE%20A%20CHINESE%20ROOM_1995.pdf
  
• Hanoch Ben-Yami (1993). A note on the chinese room.   (Abstract & more)
  Abstract:   Searle's Chinese Room was supposed to prove that computers can't understand: the man in the room, following, like a computer, syntactical rules alone, though indistinguishable from a genuine Chinese speaker, doesn't understand a word. But such a room is impossible: the man won't be able to respond correctly to questions like What is the time?, even though such an ability is indispensable for a genuine Chinese speaker. Several ways to provide the room with the required ability are considered, and it is concluded that for each of these the room will have understanding. Hence, Searle's argument is invalid
  Additional links for this entry:
  http://www.springerlink.com/index/K1NX461746N57467.pdf
  
• Selmer Bringsjord & Ron Noel (2003). Real robots and the missing thought-experiment in the chinese room dialectic.   (More)
  Additional links for this entry:
  http://kryten.mm.rpi.edu/searlebook1.pdf
  http://citeseer.ist.psu.edu/bringsjord99real.html
  
• Steven Ravett Brown (2000). Peirce and formalization of thought: The chinese room argument.   (Abstract & more)
  Abstract: Whether human thinking can be formalized and whether machines can think in a human sense are questions that have been addressed by both Peirce and Searle. Peirce came to roughly the same conclusion as Searle, that the digital computer would not be able to perform human thinking or possess human understanding. However, his rationale and Searle's differ on several important points. Searle approaches the problem from the standpoint of traditional analytic philosophy, where the strict separation of syntax and semantics renders understanding impossible for a purely syntactical device. Peirce disagreed with that analysis, but argued that the computer would only be able to achieve algorithmic thinking, which he considered the simplest type. Although their approaches were radically dissimilar, their conclusions were not. I will compare and analyze the arguments of both Peirce and Searle on this issue, and outline some implications of their conclusions for the field of Artificial Intelligence
  Additional links for this entry:
  http://cogprints.ecs.soton.ac.uk/archive/00001002/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/10/02/
  http://cogprints.ecs.soton.ac.uk/archive/00001002/00/Peirce_Paper.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1002
  http://cogprints.org/1002/1/Peirce_Paper.html
  http://cogprints.org/1002/0/Peirce_Paper.html
  
• Graham Button, Jeff Coutler & John R. E. Lee (2000). Re-entering the chinese room: A reply to Gottfried and Traiger.   (More)
  Additional links for this entry:
  http://www.kluweronline.com/article.asp?PIPS=238829&PDF=1
  http://www.springerlink.com/index/JQ41061085707655.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000001/00238829
  
• David J. Chalmers (1992). Subsymbolic computation and the chinese room.   (Abstract & more)
  Abstract: More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power
  Additional links for this entry:
  http://consc.net/papers/subsymbolic.pdf
  http://citeseer.ist.psu.edu/chalmers92subsymbolic.html
  http://www.u.arizona.edu/~chalmers/papers/subsymbolic.pdf
  
• Paul M. Churchland & Patricia S. Churchland (1990). Could a machine think?   (More)
  Additional links for this entry:
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=2294584&dopt=Citation
  
• David J. Cole (1991). Artificial intelligence and personal identity.   (Abstract & more)
  Abstract:   Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them
  Additional links for this entry:
  http://www.springerlink.com/content/t130331427116747/fulltext.pdf
  http://www.springerlink.com/index/T130331427116747.pdf
  
• David Cole (online). The chinese room argument.   (More)
• B. Jack Copeland (1993). The curious case of the chinese gym.   (Abstract & more)
  Abstract:   Searle has recently used two adaptations of his Chinese room argument in an attack on connectionism. I show that these new forms of the argument are fallacious. First I give an exposition of and rebuttal to the original Chinese room argument, and then a brief introduction to the essentials of connectionism
  Additional links for this entry:
  http://home.swipnet.se/drofe/chincym.pdf
  http://www.alanturing.net/turing_archive/pages/pub/chincym/chincym.pdf
  http://www.springerlink.com/content/ut111604435042jn/fulltext.pdf
  http://www.springerlink.com/index/UT111604435042JN.pdf
  
• Dr Louis J. Cutrona, Jr (ms). Zombies in Searle's chinese room: Putting the Turing test to bed.   (Abstract & more)
  Abstract: Searle’s discussions over the years 1980-2004 of the implications of his “Chinese Room” Gedanken experiment are frustrating because they proceed from a correct assertion: (1) “Instantiating a computer program is never by itself a sufficient condition of intentionality;” and an incorrect assertion: (2) “The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program.” In this article, I describe how to construct a Gedanken zombie Chinese Room program that will pass the Turing test and at the same time unambiguously demonstrates the correctness of (1). I then describe how to construct a Gedanken Chinese brain program that will pass the Turing test, has a mind, and understands Chinese, thus demonstrating that (2) is incorrect. Searle’s instantiation of this program can and does produce intentionality. Searle’s longstanding ignorance of Chinese is simply irrelevant and always has been. I propose a truce and a plan for further exploration
  Additional links for this entry:
  http://cogprints.org/4636/1/TR%2D05%2D002.pdf
  
• Robert I. Damper (2004). The chinese room argument--dead but not yet buried.   (More)
  Additional links for this entry:
  http://eprints.resist.ecs.soton.ac.uk/9561/
  http://eprints.ecs.soton.ac.uk/9561/01/damper.pdf
  http://www.ingentaconnect.com/content/imp/jcs/2004/00000011/F0020005/art00010
  
• Michael G. Dyer (1990). Finding lost minds.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/777957611.pdf
  
• Michael G. Dyer (1990). Intentionality and computationalism: Minds, machines, Searle and Harnad.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/777957609.pdf
  
• Ruth G. Millikan (2005). Some reflections on the theory theory - simulation theory discussion.   (More)
• Simone Gozzano (1997). The chinese room argument: Consciousness and understanding.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=VHZy4j_Qa-cC&oi=fnd&pg=PA231&ots=RjBGkheslj&sig=o6qlThOEXlScQeLhIvbrclE6lQc
  
• David Harrison (1997). Connectionism hits the chinese gym.   (More)
• Stevan Harnad (1990). Lost in the hermeneutic hall of mirrors.   (Abstract & more)
  Abstract: Critique of Computationalism as merely projecting hermeneutics (i.e., meaning originating from the mind of an external interpreter) onto otherwise intrinsically meaningless symbols. Projecting an interpretation onto a symbol system results in its being reflected back, in a spuriously self-confirming way
  Additional links for this entry:
  http://cogprints.org/1577/1/harnad90.dyer.crit.html
  http://cogprints.org/1577/0/harnad90.dyer.crit.html
  
• Stevan Harnad (1989). Minds, machines and Searle.   (Abstract & more)
  Abstract: Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem
  Additional links for this entry:
  http://cogprints.org/1573/
  http://eprints.ecs.soton.ac.uk/1891/
  http://eprints.resist.ecs.soton.ac.uk/1891/
  http://citeseer.ist.psu.edu/harnad89minds.html
  http://cogprints.org/1573/00/harnad89.searle.html
  http://cogprints.ecs.soton.ac.uk/archive/00001573/
  http://cogprints.ecs.soton.ac.uk/archive/00001622/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/73/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/73/
  http://eprints.ecs.soton.ac.uk/1891/01/harnad89.searle.html
  http://eprints.ecs.soton.ac.uk/5942/01/harnad00.searle.html
  http://eprints.ecs.soton.ac.uk/1891/01/harnad89.searle.html
  http://cogsci.soton.ac.uk/harnad/Papers/Harnad/harnad89.searle.html
  http://cogsci.soton.ac.uk/harnad/Papers/Harnad/harnad89.searle.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad00.searle.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad89.searle.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad89.searle.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad00.searle.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad89.searle.html
  http://eprints.ecs.soton.ac.uk/archive/00005942/01/harnad00.searle.html
  http://cogprints.ecs.soton.ac.uk/archive/00001622/00/harnad00.searle.html
  http://cogprints.ecs.soton.ac.uk/archive/00001573/00/harnad89.searle.html
  http://cogprints.ecs.soton.ac.uk/archive/00001573/00/harnad89.searle.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1573
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2229271CI
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1573
  http://web.comlab.ox.ac.uk/oucl/research/areas/ieg/e-library/sources/harnad89_searle.pdf
  http://www.informaworld.com/index/777580727.pdf
  http://www.ecs.soton.ac.uk/%7Eharnad/Papers/Harnad/harnad89.searle.html
  http://cogprints.org/1573/1/harnad89.searle.html
  http://cogprints.org/1573/0/harnad89.searle.html
  
• Stevan Harnad (2003). Minds, machines, and Searle 2: What's right and wrong about the chinese room argument.   (Abstract & more)
  Abstract: When in 1979 Zenon Pylyshyn, associate editor of Behavioral and Brain Sciences (BBS, a peer commentary journal which I edit) informed me that he had secured a paper by John Searle with the unprepossessing title of [XXXX], I cannot say that I was especially impressed; nor did a quick reading of the brief manuscript -- which seemed to be yet another tedious "Granny Objection"[1] about why/how we are not computers -- do anything to upgrade that impression
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/5942/01/harnad00.searle.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad00.searle.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad00.searle.html
  http://eprints.ecs.soton.ac.uk/archive/00005942/01/harnad00.searle.html
  http://cogprints.ecs.soton.ac.uk/archive/00001622/00/harnad00.searle.html
  
• Stevan Harnad (2001). Rights and wrongs of Searle's chinese room argument.   (Abstract & more)
  Abstract: "in an academic generation a little overaddicted to "politesse," it may be worth saying that violent destruction is not necessarily worthless and futile. Even though it leaves doubt about the right road for London, it helps if someone rips up, however violently, a
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/5942/01/harnad00.searle.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad00.searle.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad00.searle.html
  
• Stevan Harnad, Searle's chinese room argument.   (Abstract & more)
  Abstract: Computationalism. According to computationalism, to explain how the mind works, cognitive science needs to find out what the right computations are -- the same ones that the brain performs in order to generate the mind and its capacities. Once we know that, then every system that performs those computations will have those mental states: Every computer that runs the mind's program will have a mind, because computation is hardware independent : Any hardware that is running the right program has the right computational states
• Stevan Harnad (2001). What's wrong and right about Searle's chinese room argument?   (Abstract & more)
  Abstract: Searle's Chinese Room Argument showed a fatal flaw in computationalism (the idea that mental states are just computational states) and helped usher in the era of situated robotics and symbol grounding (although Searle himself thought neuroscience was the only correct way to understand the mind)
  Additional links for this entry:
  http://cogprints.org/4023/1/searlbook.htm
  http://cogprints.org/1622/0/harnad00.searle.html
  http://cogprints.soton.ac.uk/documents/disk0/00/00/16/22/
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:1622
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1622
  http://cogprints.org/1622/1/harnad00.searle.html
  
• Larry Hauser (online). Chinese room argument.   (More)
• Larry Hauser (2003). Nixin' goes to china.   (Abstract & more)
  Abstract: The intelligent-seeming deeds of computers are what occasion philosophical debate about artificial intelligence (AI) in the first place. Since evidence of AI is not bad, arguments against seem called for. John Searle's Chinese Room Argument (1980a, 1984, 1990, 1994) is among the most famous and long-running would-be answers to the call. Surprisingly, both the original thought experiment (1980a) and Searle's later would-be formalizations of the embedding argument (1984, 1990) are quite unavailing against AI proper (claims that computers do or someday will think ). Searle lately even styles it a "misunderstanding" (1994, p. 547) to think the argument was ever so directed! The Chinese room is now advertised to target Computationalism (claims that computation is what thought essentially is ) exclusively. Despite its renown, the Chinese Room Argument is totally ineffective even against this target
• Larry Hauser (1993). Searle's Chinese Box: The Chinese Room Argument and Artificial Intelligence.   (More)
• Larry Hauser (1997). Searle's chinese box: Debunking the chinese room argument.   (Abstract & more)
  Abstract: John Searle's Chinese room argument is perhaps the most influential and widely cited argument against artificial intelligence (AI). Understood as targeting AI proper -- claims that computers can think or do think -- Searle's argument, despite its rhetorical flash, is logically and scientifically a dud. Advertised as effective against AI proper, the argument, in its main outlines, is an ignoratio elenchi. It musters persuasive force fallaciously by indirection fostered by equivocal deployment of the phrase "strong AI" and reinforced by equivocation on the phrase "causal powers (at least) equal to those of brains." On a more carefully crafted understanding -- understood just to target metaphysical identification of thought with computation ("Functionalism" or "Computationalism") and not AI proper -- the argument is still unsound, though more interestingly so. It's unsound in ways difficult for high church -- "someday my prince of an AI program will come" -- believers in AI to acknowledge without undermining their high church beliefs. The ad hominem bite of Searle's argument against the high church persuasions of so many cognitive scientists, I suggest, largely explains the undeserved repute this really quite disreputable argument enjoys among them
  Additional links for this entry:
  http://cogprints.org/240/00/199802002.html
  http://members.aol.com/lshauser/chiboxab.html
  http://members.aol.com/lshauser2/chinabox.html
  http://members.aol.com/wutsamada/disserta.html
  http://members.aol.com/lshauser/chiboxab.html
  http://members.aol.com/wutsamada/contents.html
  http://members.aol.com/lshauser2/chinabox.html
  http://cogprints.ecs.soton.ac.uk/archive/00000240/
  http://portal.acm.org/citation.cfm?id=596705.596737
  http://cogprints.soton.ac.uk/documents/disk0/00/00/02/40/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/02/40/
  http://cogprints.ecs.soton.ac.uk/archive/00000240/00/199802002.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:240
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:240
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=322378CI
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=322378CI
  http://www.springerlink.com/content/kx8r435770352167/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=120195&PDF=1
  http://www.kluweronline.com/article.asp?PIPS=120195&PDF=1
  http://www.springerlink.com/index/KX8R435770352167.pdf
  http://www.springerlink.com/index/KX8R435770352167.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000002/00120195
  http://cogprints.org/240/1/199802002.html
  http://cogprints.org/240/0/199802002.html
  
• Larry Hauser (online). Searle's chinese room argument.   (Abstract & more)
  Abstract: John Searle's 1980a) thought experiment and associated 1984a) argument is one of the best known and widely credited counters to claims of artificial intelligence (AI), i.e., to claims that computers _do_ or at least _can_ (roughly, someday will) think. According to Searle's original presentation, the argument is based on two truths: _brains cause minds_ , and _syntax doesn't suffice_ _for semantics_ . Its target, Searle dubs "strong AI": "according to strong AI," according to Searle, "the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really _is_ a mind in the sense that computers given the right programs can be literally said to _understand_ and have other cognitive states" 1980a, p. 417). Searle contrasts "strong AI" to "weak AI". According to weak AI, according to Searle, computers just
• Larry Hauser (online). The chinese room argument.   (Abstract & more)
  Abstract: _The Chinese room argument_ - John Searle's (1980a) thought experiment and associated (1984) derivation - is one of the best known and widely credited counters to claims of artificial intelligence (AI), i.e., to claims that computers _do_ or at least _can_ (someday might) think. According to Searle's original presentation, the argument is based on two truths: _brains cause minds_ , and _syntax doesn't_ _suffice for semantics_ . Its target, Searle dubs "strong AI": "according to strong AI," according to Searle, "the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really _is_ a mind in the sense that computers given the right programs can be literally said to _understand_ and have other cognitive states" (1980a, p. 417). Searle contrasts "strong AI" to "weak AI". According to weak AI, according to Searle, computers just
• Patrick Hayes, Stevan Harnad, Donald R. Perlis & Ned Block (1992). Virtual symposium on virtual mind.   (Abstract & more)
  Abstract: When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual mind" real? This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one
  Additional links for this entry:
  http://eprints.resist.ecs.soton.ac.uk/3371/
  http://eprints.ecs.soton.ac.uk/archive/00003371/
  http://eprints.ecs.soton.ac.uk/archive/00003371/
  http://cogprints.org/1585/00/harnad92.virtualmind.html
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/85/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/85/
  http://cogsci.soton.ac.uk/harnad/Papers/Harnad/harnad92.virtualmind.html
  http://cogsci.soton.ac.uk/harnad/Papers/Harnad/harnad92.virtualmind.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad92.virtualmind.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad92.virtualmind.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad92.virtualmind.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad92.virtualmind.html
  http://eprints.ecs.soton.ac.uk/archive/00003371/02/harnad92.virtualmind.html
  http://cogprints.ecs.soton.ac.uk/archive/00001585/00/harnad92.virtualmind.html
  http://cogprints.ecs.soton.ac.uk/archive/00001585/00/harnad92.virtualmind.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:3371
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1585
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1585
  http://www.springerlink.com/content/g2063372459ww766/fulltext.pdf
  http://www.springerlink.com/index/G2063372459WW766.pdf
  http://cogprints.org/1585/1/harnad92.virtualmind.html
  http://cogprints.org/1585/0/harnad92.virtualmind.html
  
• C. Kaernbach (2005). No virtual mind in the chinese room.   (More)
  Additional links for this entry:
  http://www.psychologie.uni-kiel.de/emotion/team/kaernbach/publications/2005_kae_jcs.pdf
  http://www.ingentaconnect.com/content/imp/jcs/2005/00000012/00000011/art00002
  
• Robert W. Kentridge (2001). Computation, chaos and non-deterministic symbolic computation: The chinese room problem solved?   (More)
  Additional links for this entry:
  http://cogsci.soton.ac.uk/~harnad/Temp/Think/kentridg.htm
  http://www.cogsci.ecs.soton.ac.uk/cgi/psyc/newpsy?article=12.050
  http://www.cogsci.ecs.soton.ac.uk/cgi/psyc/newpsy?symbolism-connectionism.17
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.kentridge.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.kentridge.html
  http://psycprints.ecs.soton.ac.uk/archive/00000179/02/psyc.01.12.050.symbolism-connectionism.17.kentridge
  
• Kevin B. Korb (1991). Searle's AI program.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/776648442.pdf
  
• Diane Law (online). Searle, subsymbolic functionalism, and synthetic intelligence.   (More)
  Additional links for this entry:
  http://nn.cs.utexas.edu/downloads/papers/law.synthetic.pdf
  http://nn.cs.utexas.edu/downloads/papers/law.synthetic.ps.gz
  http://www.cs.utexas.edu/users/nn/downloads/papers/law.synthetic.pdf
  http://www.cs.utexas.edu/ftp/pub/neural-nets/papers/law.synthetic.pdf
  http://www.let.rug.nl/~nerbonne/teach/neuro/kleiweg/law.synthetic.ps.gz
  http://odur.let.rug.nl/~nerbonne/teach/neuro/kleiweg/law.synthetic.ps.gz
  http://historical.ncstrl.org/litesite-data/utexas_cs/UT-AI-TR-94-222.ps.Z
  
• Alan M. Leslie & Brian J. Scholl (1999). Modularity, development and 'theory of mind'.   (Abstract & more)
  Abstract: Psychologists and philosophers have recently been exploring whether the mechanisms which underlie the acquisition of ‘theory of mind’ (ToM) are best charac- terized as cognitive modules or as developing theories. In this paper, we attempt to clarify what a modular account of ToM entails, and why it is an attractive type of explanation. Intuitions and arguments in this debate often turn on the role of develop- ment: traditional research on ToM focuses on various developmental sequences, whereas cognitive modules are thought to be static and ‘anti-developmental’. We suggest that this mistaken view relies on an overly limited notion of modularity, and we explore how ToM might be grounded in a cognitive module and yet still afford development. Modules must ‘come on-line’, and even fully developed modules may still develop internally, based on their constrained input. We make these points con- crete by focusing on a recent proposal to capture the development of ToM in a module via parameterization
  Additional links for this entry:
  http://l3d.cs.colorado.edu/~ctg/classes/lib/cogsci/scholl.pdf
  http://ruccs.rutgers.edu/~aleslie/99-Scholl-Leslie-MindLang.pdf
  http://ruccs.rutgers.edu/~aleslie/99-Scholl-Leslie-MindLang.pdf
  http://www.cs.colorado.edu/~duck/ctg/classes/lib/cogsci/scholl.pdf
  http://www.blackwell-synergy.com/doi/abs/10.1111/1468-0017.00106?favorite=add
  http://www.blackwell-synergy.com/links/doi/10.1111/1468-0017.00106
  http://www.ingentaconnect.com/content/bpl/mila/1999/00000014/00000001/art00106
  
• John McCarthy (online). John Searle's chinese room argument.   (Abstract & more)
  Abstract: John Searle begins his (1990) ``Consciousness, Explanatory Inversion and Cognitive Science'' with

  ``Ten years ago in this journal I published an article (Searle, 1980a and 1980b) criticising what I call Strong
  AI, the view that for a system to have mental states it is sufficient for the system to implement the right sort of
  program with right inputs and outputs. Strong AI is rather easy to refute and the basic argument can be
  summarized in one sentence: {it a system, me for example, could implement a program for understanding
  Chinese, for example, without understanding any Chinese at all.} This idea, when developed, became
  known as the Chinese Room Argument.''

  The Chinese Room Argument can be refuted in one sentence
• Ethan Mitchell (2008). The real Chinese Room.   (More)
• William J. Rapaport (2006). How Helen Keller used syntactic semantics to escape from a chinese room.   (Abstract & more)
  Abstract:   A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS computational knowledge-representation system, the essay analyzes Keller’s belief that learning that “everything has a name” was the key to her success, enabling her to “partition” her mental concepts into mental representations of: words, objects, and the naming relations between them. It next looks at Herbert Terrace’s theory of naming, which is akin to Keller’s, and which only humans are supposed to be capable of. The essay suggests that computers at least, and perhaps non-human primates, are also capable of this kind of naming
  Additional links for this entry:
  http://www.springerlink.com/content/601p84g288756774/fulltext.pdf
  
• John R. Searle (1990). Is the brain's mind a computer program?   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=hQCzOmaGeVYC&oi=fnd&pg=PA264&ots=j6aih-FnAo&sig=S9z08wWdwwt3psKgZPsRfS-1bXE
  
• John R. Searle (1980). Minds, brains and programs.   (Abstract & more)
  Abstract: What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI (artificial intelligence). According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to..
  Additional links for this entry:
  http://courses.washington.edu/info300/searle.pdf
  http://pami.uwaterloo.ca/tizhoosh/docs/Searle.pdf
  http://www.ifispan.waw.pl/~rpilat/john_searle.pdf
  http://members.aol.com/NeoNoetics/MindsBrainsPrograms.html
  http://www.bbsonline.org/Preprints/OldArchive/bbs.searle2.html
  http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdcPQ709B&sig=IAV-tI3UMUrdII-jhXVCiE5EiNY
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dLmgQW13bB&sig=e696dd_RJUp-z7nJq_VUVkJ6Vco
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdgLT5-bF&sig=WhrAW9GsnXpijAGLjoTnMSl83U4
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdeTS616J&sig=Gup1URzClmw94-tEdiKkRv8oo_E
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdfPT1-fI&sig=NuhA3CnAPDnsWqo8Ta8-RX2cCEY
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dLmgLS63cA&sig=lNvUnhfC6Xi8TMG19ynGzF7-UrE
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdeNS1ZcF&sig=6EzNiu7H_1bXLA0zJ16b39NuIVQ
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdiPV7_dJ&sig=xzN-ebbJvEML3FULifpKsvbJ4B0
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dLmgOW03aF&sig=v25MYgLHP7PRb5D3u6V0j21p3Os
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdhTV4ZdG&sig=_xvxtHTY2CziSv0BQSRuTeAcVyE
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMddRR63eF&sig=LYLLmE8CSwbq9blXRhOQWNiQKBo
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMddLR2XfE&sig=hPqWXhv4YdFnGCjtMhQpRctFDz8
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdgRU01eJ&sig=PuxfSwx103nfQfLP8luU4Z-hgSc
  http://books.google.com/books?hl=en&lr=&id=CEMYUU_HFMAC&oi=fnd&pg=PA201&ots=dMdhNV0_7I&sig=JxSMo440evbsIv2dv4QpEJVAC4A
  
• Aaron Sloman (1986). Did Searle attack strong strong AI or weak strong AI?   (More)
  Additional links for this entry:
  http://www.cs.bham.ac.uk/research/cogaff/sloman.searle.85.text
  
• Mark D. Sprevak (online). Algorithms and the chinese room.   (More)
• Jerome C. Wakefield (2003). The chinese room argument reconsidered: Essentialism, indeterminacy, and strong AI.   (Abstract & more)
  Abstract:   I argue that John Searle's (1980) influential Chinese room argument (CRA) against computationalism and strong AI survives existing objections, including Block's (1998) internalized systems reply, Fodor's (1991b) deviant causal chain reply, and Hauser's (1997) unconscious content reply. However, a new ``essentialist'' reply I construct shows that the CRA as presented by Searle is an unsound argument that relies on a question-begging appeal to intuition. My diagnosis of the CRA relies on an interpretation of computationalism as a scientific theory about the essential nature of intentional content; such theories often yield non-intuitive results in non-standard cases, and so cannot be judged by such intuitions. However, I further argue that the CRA can be transformed into a potentially valid argument against computationalism simply by reinterpreting it as an indeterminacy argument that shows that computationalism cannot explain the ordinary distinction between semantic content and sheer syntactic manipulation, and thus cannot be an adequate account of content. This conclusion admittedly rests on the arguable but plausible assumption that thought content is interestingly determinate. I conclude that the viability of computationalism and strong AI depends on their addressing the indeterminacy objection, but that it is currently unclear how this objection can be successfully addressed
  Additional links for this entry:
  http://www.springerlink.com/content/q76u712262711110/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5115346&PDF=1
  http://www.springerlink.com/index/Q76U712262711110.pdf
  http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000002/05115346
  

Machine Consciousness

• M. E. Tson (ms). A Brief Explanation of Consciousness.   (Abstract & more)
  Abstract: This short paper (4 pages) demonstrates how subjective experience, language, and consciousness can be explained in terms of abilities we share with the simplest of creatures, specifically the ability to detect, react to, and associate various aspects of the world.
• William Y. Adams (online). Intersubjective transparency and artificial consciousness.   (More)
• Igor L. Aleksander (2007). Machine consciousness.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=YHGacGKyVbYC&oi=fnd&pg=PA99&ots=SSSQPkbxBS&sig=LmbDUzP5IccBZCh8TG02TqZZL28
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16186018&dopt=Citation
  http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=16186018&cmd=showdetailview&indexed=google
  
• Igor L. Aleksander (2006). Machine consciousness.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=YHGacGKyVbYC&oi=fnd&pg=PA99&ots=SSSQPkbzBY&sig=dSV8_pheTWTgsxz7NiRaIDipYNs
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16186018&dopt=Citation
  http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=16186018&cmd=showdetailview&indexed=google
  
• Richard L. Amoroso (1997). The theoretical foundations for engineering a conscious quantum computer.   (More)
  Additional links for this entry:
  http://www.mindspring.com/~noetic.advanced.studies/Amoroso19.pdf
  
• R. Arrabales & A. Sanchis (forthcoming). Applying machine consciousness models in autonomous situated agents.   (More)
• John L. Bell (online). Algorithmicity and consciousness.   (Abstract & more)
  Abstract: Why should one believe that conscious awareness is solely the result of organizational complexity? What is the connection between consciousness and combinatorics: transformation of quantity into quality? The claim that the former is reducible to the other seems unconvincing—as unlike as chalk and cheese! In his book1 Penrose is at least attempting to compare like with like: the enigma of consciousness with the progress of physics
• Pierre Bonzon (2003). Conscious Behavior through Reflexive Dialogs.   (Abstract & more)
  Abstract: We consider the problem of executing conscious behavior i.e., of driving an agent’s actions and of allowing it, at the same time, to run concurrent processes reflecting on these actions. Toward this end, we express a single agent’s plans as reflexive dialogs in a multi-agent system defined by a virtual machine. We extend this machine’s planning language by introducing two specific operators for reflexive dialogs i.e., conscious and caught for monitoring beliefs and actions, respectively. The possibility to use the same language both to drive a machine and to establish a reflexive communication within the machine itself stands as a key feature of our model.
• Selmer Bringsjord (1994). Could, how could we tell if, and should - androids have inner lives?   (More)
• Selmer Bringsjord (2004). On building robot persons: Response to Zlatev.   (Abstract & more)
  Abstract:   Zlatev offers surprisingly weak reasoning in support of his view that robots with the right kind of developmental histories can have meaning. We ought nonetheless to praise Zlatev for an impressionistic account of how attending to the psychology of human development can help us build robots that appear to have intentionality
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=1011949.1011965
  http://www.cogs.indiana.edu/cogx/robotperson_Zlatev_response.pdf
  http://www.springerlink.com/content/w83402573w376q55/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5274048&PDF=1
  http://www.springerlink.com/index/W83402573W376Q55.pdf
  http://www.ingentaconnect.com/content/klu/mind/2004/00000014/00000003/05274048
  
• G. Buttazzo (2001). Artificial consciousness: Utopia or real possibility?   (More)
  Additional links for this entry:
  http://feanor.sssup.it/~giorgio/paps/2001/ieeecm01.pdf
  http://csdl.computer.org/comp/mags/co/2001/07/r7024abs.htm
  http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=933500
  http://www.das.ufsc.br/~rabelo/Ensino/DAS6607/Artigos-Gerais/ArtificialConsciousness.pdf
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=20061053096677MT&recid=20061090133720CI&recid=20061023229829EA
  
• G. Caplain (1995). Is consciousness a computational property?   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=VHZy4j_Qa-cC&oi=fnd&pg=PA190&ots=RjBGledukl&sig=Oa-FArSWce_2jXSLqmlPHTdqlZc
  
• Rodney M. J. Cotterill (2003). Cyberchild: A simulation test-bed for consciousness studies.   (More)
• Daniel C. Dennett (1997). Consciousness in Human and Robot Minds.   (Abstract & more)
  Abstract: The best reason for believing that robots might some day become conscious is that we human beings are conscious, and we are a sort of robot ourselves. That is, we are extraordinarily complex self-controlling, self-sustaining physical mechanisms, designed over the eons by natural selection, and operating according to the same well-understood principles that govern all the other physical processes in living things: digestive and metabolic processes, self-repair and reproductive processes, for instance. It may be wildly over-ambitious to suppose that human artificers can repeat Nature's triumph, with variations in material, form, and design process, but this is not a deep objection. It is not as if a conscious machine contradicted any fundamental laws of nature, the way a perpetual motion machine does. Still, many skeptics believe--or in any event want to believe--that it will never be done. I wouldn't wager against them, but my reasons for skepticism are mundane, economic reasons, not theoretical reasons
  Additional links for this entry:
  http://ase.tufts.edu/cogstud/papers/concrobt.htm
  http://cogprints.ecs.soton.ac.uk/archive/00000429/
  http://cogprints.ecs.soton.ac.uk/archive/00000429/00/concrobt.htm
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:429
  
• Daniel C. Dennett (1994). The practical requirements for making a conscious robot.   (Abstract & more)
  Abstract: Arguments about whether a robot could ever be conscious have been conducted up to now in the factually impoverished arena of what is possible "in principle." A team at MIT of which I am a part is now embarking on a longterm project to design and build a humanoid robot, Cog, whose cognitive talents will include speech, eye-coordinated manipulation of objects, and a host of self-protective, self-regulatory and self-exploring activities. The aim of the project is not to make a conscious robot, but to make a robot that can interact with human beings in a robust and versatile manner in real time, take care of itself, and tell its designers things about itself that would otherwise be extremely difficult if not impossible to determine by examination. Many of the details of Cog's "neural" organization will parallel what is known (or presumed known) about their counterparts in the human brain, but the intended realism of Cog as a model is relatively coarse-grained, varying opportunistically as a function of what we think we know, what we think we can build, and what we think doesn't matter. Much of what we think will of course prove to be mistaken; that is one advantage of real experiments over thought experiments
  Additional links for this entry:
  http://adsabs.harvard.edu/abs/1994RSPTA.349..133D
  http://www.journals.royalsoc.ac.uk/index/L62130158Q786731.pdf
  http://cogsci.soton.ac.uk/~harnad/Papers/Py104/dennett.rob.html
  http://links.jstor.org/sici?sici=0962-8428(19941015)349:1689<133:TPRFMA>2.0.CO;2-1
  
• Włodzisław Duch (2005). Brain-inspired conscious computing architecture.   (Abstract & more)
  Abstract: What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human
  Additional links for this entry:
  http://citeseer.ist.psu.edu/duch05braininspired.html
  http://www.phys.uni.torun.pl/publications/kmk/03-Brainins.pdf
  http://www.phys.uni.torun.pl/publications/kmk/03-Brainins.html
  http://cogprints.ecs.soton.ac.uk/archive/00003319/01/03-Brainins.pdf
  http://cogprints.org/3319/1/03-Brainins.pdf
  http://cogprints.org/3319/1/03%2DBrainins.pdf
  
• Stan Franklin (2003). Ida: A conscious artifact?   (More)
• Stevan Harnad (2003). Can a machine be conscious? How?   (Abstract & more)
  Abstract: A "machine" is any causal physical system, hence we are machines, hence machines can be conscious. The question is: which kinds of machines can be conscious? Chances are that robots that can pass the Turing Test -- completely indistinguishable from us in their behavioral capacities -- can be conscious (i.e. feel), but we can never be sure (because of the "other-minds" problem). And we can never know HOW they have minds, because of the "mind/body" problem. We can only know how they pass the Turing Test, but not how, why or whether that makes them feel
  Additional links for this entry:
  http://cogprints.org/2460/
  http://eprints.ecs.soton.ac.uk/7718
  http://cogprints.org/5330/1/machine.htm
  http://cogprints.org/2460/1/machine.htm
  http://eprints.resist.ecs.soton.ac.uk/7718/
  http://eprints.ecs.soton.ac.uk/archive/00007718/
  http://cogsci.soton.ac.uk/~harnad/Temp/machine.htm
  http://cogprints.ecs.soton.ac.uk/archive/00002460/
  http://www.ecs.soton.ac.uk/~harnad/Temp/machine.htm
  http://users.ecs.soton.ac.uk/~harnad/Temp/machine.htm
  http://cogprints.ecs.soton.ac.uk/archive/00002460/01/machine.htm
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:2460
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:2460
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1347
  http://users.ecs.soton.ac.uk/%7Eharnad/Temp/machine.htm
  
• Owen Holland & Russell B. Goodman (2003). Robots with internal models: A route to machine consciousness?   (More)
  Additional links for this entry:
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1348
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1342
  
• William G. Lycan (1998). Qualitative experience in machines.   (More)
• Hugh J. McCann (2005). Intentional action and intending: Recent empirical studies.   (Abstract & more)
  Abstract: Recent empirical work calls into question the so-called Simple View that an agent who A’s intentionally intends to A. In experimental studies, ordinary speakers frequently assent to claims that, in certain cases, agents who knowingly behave wrongly intentionally bring about the harm they do; yet the speakers tend to deny that it was the intention of those agents to cause the harm. This paper reports two additional studies that at first appear to support the original ones, but argues that in fact, the evidence of all the studies considered is best understood in terms of the Simple View.
  Additional links for this entry:
  http://instruct1.cit.cornell.edu/courses/phi663/knobe McCann.pdf
  http://www.informaworld.com/smpp/./ftinterface~content=a727334015~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/RP912503N228W554.pdf
  http://www.informaworld.com/index/727334015.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2005/00000018/00000006/art00005
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a727334015~fulltext=713240930
  
• John McCarthy (1996). Making robots conscious of their mental states.   (Abstract & more)
  Abstract: In AI, consciousness of self consists in a program having certain kinds of facts about its own mental processes and state of mind. We discuss what consciousness of its own mental structures a robot will need in order to operate in the common sense world and accomplish the tasks humans will give it. It's quite a lot. Many features of human consciousness will be wanted, some will not, and some abilities not possessed by humans have already been found feasible and useful in limited contexts. We give preliminary fragments of a logical language a robot can use to represent information about its own state of mind. A robot will often have to conclude that it cannot decide a question on the basis of the information in memory and therefore must seek information externally. Gödel's idea of relative consistency is used to formalize non-knowledge. Programs with the kind of consciousness discussed in this article do not yet exist, although programs with some components of it exist. Thinking about consciousness with a view to designing it provides a new approach to some of the problems of consciousness studied by philosophers. One advantage is that it focusses on the aspects of consciousness important for intelligent behavior
  Additional links for this entry:
  http://citeseer.ist.psu.edu/81449.html
  http://citeseer.ist.psu.edu/mccarthy95making.html
  http://cogprints.ecs.soton.ac.uk/archive/00000422/
  http://www-formal.stanford.edu/jmc/consciousness.ps
  http://portal.acm.org/citation.cfm?id=647636.733058
  http://www-formal.stanford.edu/jmc/consciousness/consciousness.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:422
  
• Drew McDermott (2007). Artificial intelligence and consciousness.   (More)
• Alfred R. Mele (2006). The folk concept of intentional action: A commentary.   (Abstract & more)
  Abstract: In this commentary, I discuss the three main articles in this volume that present survey data relevant to a search for something that might merit the label “the folk concept of intentional action” – the articles by Joshua Knobe and Arudra Burra, Bertram Malle, and Thomas Nadelhoffer. My guiding question is this: What shape might we find in an analysis of intentional action that takes at face value the results of all of the relevant surveys about vignettes discussed in these three articles?1 To simplify exposition, I assume that there is something that merits the label I mentioned
  Additional links for this entry:
  http://www.unc.edu/~knobe/MeleJCC.pdf
  http://www.springerlink.com/index/0P30736376436T04.pdf
  http://www.ingentaconnect.com/content/brill/jocc/2006/00000006/F0020001/art00016
  
• Christophe Menant, Proposal for an approach to artificial consciousness based on self-consciousness.   (Abstract & more)
  Abstract: Current research on artificial consciousness is focused on phenomenal consciousness and on functional consciousness. We propose to shift the focus to self-consciousness in order to open new areas of investigation. We use an existing scenario where self-consciousness is considered as the result of an evolution of representations. Application of the scenario to the possible build up of a conscious robot also introduces questions relative to emotions in robots. Areas of investigation are proposed as a continuation of this approach
  Additional links for this entry:
  http://cogprints.org/6013/
  http://cogprints.org/6013/1/FS01MenantCh20070905074007.pdf
  http://www.aaai.org/Papers/Symposia/Fall/2007/FS-07-01/FS07-01-020.pdf
  http://cogprints.org/5866/3/FS01MenantCh20070905074007.pdf
  http://cogprints.org/5999/1/FS01MenantCh20070905074007.pdf
  
• Marvin L. Minsky (1991). Conscious machines.   (More)
• Marc A. Moffett & Jennifer Cole Wright (online). The folk on know-how: Why radical intellectualism does not over-intellectualize.   (Abstract & more)
  Abstract: Philosophical discussion of the nature of know-how has focused on the relation between know-how and ability. Broadly speaking, neo-Ryleans attempt to identify know-how with a certain type of ability,1 while, traditionally, intellectualists attempt to reduce it to some form of propositional knowledge. For our purposes, however, this characterization of the debate is too crude. Instead, we prefer the following more explicit taxonomy. Anti-intellectualists, as we will use the term, maintain that knowing how to ? entails the ability to ?. Dispositionalists maintain that the ability to ? is sufficient (modulo some fairly innocuous constraints) for knowing how to ?. Intellectualists, as we will use the term, deny the anti-intellectualist claim. Finally, radical intellectualists deny both the anti-intellectualist and dispositionalist claims. Pace neo-Ryleans (who in our taxonomy are those who accept both dispositionalism and anti-intellectualism), radical intellectualists maintain that the ability to ? is neither necessary nor sufficient for knowing how to ?
• Shaun Nichols (2004). Folk concepts and intuitions: From philosophy to cognitive science.   (Abstract & more)
  Abstract: Analytic philosophers have long used a priori methods to characterize folk concepts like knowledge, belief, and wrongness. Recently, researchers have begun to exploit social scientific methodologies to characterize such folk concepts. One line of work has explored folk intuitions on cases that are disputed within philosophy. A second approach, with potentially more radical implications, applies the methods of cross-cultural psychology to philosophical intuitions. Recent work suggests that people in different cultures have systematically different intuitions surrounding folk concepts like wrong, knows, and refers. A third strand of research explores the emergence and character of folk concepts in children. These approaches to characterizing folk concepts provide important resources that will supplement, and perhaps sometimes displace, a priori approaches
  Additional links for this entry:
  http://dingo.sbs.arizona.edu/~snichols/Papers/FolkConcepts.pdf
  http://matrix.aklab.psych.ubc.ca/uploads/Christine_FolkPhilosophy_TICS.pdf
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=15491906&dopt=Citation
  http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=15491906&cmd=showdetailview&indexed=google
  http://linkinghub.elsevier.com/retrieve/pii/S1364661304002360
  
• Steven Pinker (online). Could a computer ever be conscious?   (More)
• Kris Rhodes (ms). Vindication of the Rights of Machine.   (Abstract & more)
  Abstract: In this paper, I argue that certain Machines can have rights independently of whether they are sentient, or conscious, or whatever you might call it.
  Additional links for this entry:
  http://www.igradeyourpaper.com/VRMv3.doc
  
• Richard H. Schlagel (1999). Why not artificial consciousness or thought?   (Abstract & more)
  Abstract:   The purpose of this article is to show why consciousness and thought are not manifested in digital computers. Analyzing the rationale for claiming that the formal manipulation of physical symbols in Turing machines would emulate human thought, the article attempts to show why this proved false. This is because the reinterpretation of designation and meaning to accommodate physical symbol manipulation eliminated their crucial functions in human discourse. Words have denotations and intensional meanings because the brain transforms the physical stimuli received from the microworld into a qualitative, macroscopic representation for consciousness. Lacking this capacity as programmed machines, computers have no representations for their symbols to designate and mean. Unlike human beings in which consciousness and thought, with their inherent content, have emerged because of their organic natures, serial processing computers or parallel distributed processing systems, as programmed electrical machines, lack these causal capacities
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=467237CI
  http://www.springerlink.com/content/k248165u513p4746/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=187743&PDF=1
  http://www.springerlink.com/index/K248165U513P4746.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000009/00000001/00187743
  
• Mark F. Sharlow (ms). Can machines have first-person properties?   (Abstract & more)
  Abstract: One of the most important ongoing debates in the philosophy of mind is the debate over the reality of the first-person character of consciousness.[1] Philosophers on one side of this debate hold that some features of experience are accessible only from a first-person standpoint. Some members of this camp, notably Frank Jackson, have maintained that epiphenomenal properties play roles in consciousness [2]; others, notably John R. Searle, have rejected dualism and regarded mental phenomena as entirely biological.[3] In the opposite camp are philosophers who hold that all mental capacities are in some sense computational - or, more broadly, explainable in terms of features of information processing systems.[4] Consistent with this explanatory agenda, members of this camp normally deny that any aspect of mind is accessible solely from a first-person standpoint. This denial sometimes goes very far - even as far as Dennett's claim that the phenomenology of conscious experience does not really exist
• Aaron Sloman & Ronald L. Chrisley (2003). Virtual machines and consciousness.   (More)
  Additional links for this entry:
  http://citeseer.ist.psu.edu/sloman02virtual.html
  http://www.cs.bham.ac.uk/research/cogaff/sloman-chrisley-jcs.pdf
  http://www.cs.bham.ac.uk/research/cogaff/sloman-chrisley-jcs03.pdf
  http://www.cs.memphis.edu/~franklin/documents/sloman-chrisley-jcs.pdf
  http://www.msci.memphis.edu/~franklin/documents/sloman-chrisley-jcs.pdf
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1350
  
• Vincenzo Tagliasco (2007). Artificial consciousness: A technological discipline.   (More)
• William I. Thompson (2003). The Borg or Borges?   (More)
  Additional links for this entry:
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1352
  
• M. E. Tson (ms). From Dust to Descartes: A Mechanical and Evolutionary Explanation of Consciousness and Self-Awareness.   (Abstract & more)
  Abstract: Beginning with physical reactions as simple and mechanical as rust, From Dust to Descartes goes step by evolutionary step to explore how the most remarkable and personal aspects of consciousness have arisen, how our awareness of the world of ourselves differs from that of other species, and whether machines could ever become self-aware. Part I addresses a newborn’s innate abilities. Part II shows how with these and experience, we can form expectations about the world. Parts III concentrates on the essential role that others play in the formation of self-awareness. Part IV then explores what follows from this explanation of human consciousness, touching on topics such as free will, personality, intelligence, and color perception which are often associated with self-awareness and the philosophy of mind.

Machine Mentality, Misc

• John Beloff (2002). Minds or machines.   (More)
• Margaret A. Boden (1995). Could a robot be creative--and would we know?   (More)
  Additional links for this entry:
  http://kogs-www.informatik.uni-hamburg.de/~neumann/Denkmaschinen-SS-2005/Vortrag-14.6.05.pdf
  
• Nick Bostrom (2003). Taking intelligent machines seriously: Reply to critics.   (Abstract & more)
  Abstract: In an earlier paper in this journal[1], I sought to defend the claims that (1) substantial probability should be assigned to the hypothesis that machines will outsmart humans within 50 years, (2) such an event would have immense ramifications for many important areas of human concern, and that consequently (3) serious attention should be given to this scenario. Here, I will address a number of points made by several commentators
  Additional links for this entry:
  http://www.questia.com/PM.qst?a=o&se=gglsc&d=5002030442
  http://linkinghub.elsevier.com/retrieve/pii/S0016328703000466
  http://www.ingentaconnect.com/content/els/00163287/2003/00000035/00000008/art00046
  
• Selmer Bringsjord (1998). Cognition is not computation: The argument from irreversibility.   (Abstract & more)
  Abstract:   The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine
  Additional links for this entry:
  http://www.rpi.edu/~brings/SELPAP/irr.ps
  http://www.rpi.edu/~brings/SELPAP/irr.pdf
  http://citeseer.ist.psu.edu/bringsjord96cognition.html
  http://www.springerlink.com/content/n887505850j434ur/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=152330&PDF=1
  http://www.springerlink.com/index/N887505850J434UR.pdf
  http://www.ingentaconnect.com/content/klu/synt/1997/00000113/00000002/00152330
  
• Selmer Bringsjord (1994). Precis of What Robots Can and Can't Be.   (More)
• William A. Dembski (1999). Are we spiritual machines?   (Abstract & more)
  Abstract: For two hundred years materialist philosophers have argued that man is some sort of machine. The claim began with French materialists of the Enlightenment such as Pierre Cabanis, Julien La Mettrie, and Baron d’Holbach (La Mettrie even wrote a book titled Man the Machine). Likewise contemporary materialists like Marvin Minsky, Daniel Dennett, and Patricia Churchland claim that the motions and modifications of matter are sufficient to account for all human experiences, even our interior and cognitive ones. Whereas the Enlightenment philosophes might have thought of humans in terms of gear mechanisms and fluid flows, contemporary materialists think of humans in terms of neurological systems and computational devices. The idiom has been updated, but the underlying impulse to reduce mind to matter remains unchanged
• Daniel C. Dennett (1997). Did Hal committ murder?   (Abstract & more)
  Abstract: The first robot homicide was committed in 1981, according to my files. I have a yellowed clipping dated 12/9/81 from the Philadelphia Inquirer--not the National Enquirer--with the headline: Robot killed repairman, Japan reports The story was an anti-climax: at the Kawasaki Heavy Industries plant in Akashi, a malfunctioning robotic arm pushed a repairman against a gearwheel-milling machine, crushing him to death. The repairman had failed to follow proper instructions for shutting down the arm before entering the workspace. Why, indeed, had this industrial accident in Japan been reported in a Philadelphia newspaper? Every day somewhere in the world a human worker is killed by one machine or another. The difference, of course, was that in the public imagination at least, this was no ordinary machine; this was a robot, a machine that might have a mind, might have evil intentions, might be capable not just of homicide but of murder
• Eric Drexler (1986). Thinking machines.   (More)
• Sanford C. Goldberg (1997). The very idea of computer self-knowledge and self-deception.   (Abstract & more)
  Abstract:   Do computers have beliefs? I argue that anyone who answers in the affirmative holds a view that is incompatible with what I shall call the commonsense approach to the propositional attitudes. My claims shall be two. First,the commonsense view places important constraints on what can be acknowledged as a case of having a belief. Second, computers – at least those for which having a belief would be conceived as having a sentence in a belief box – fail to satisfy some of these constraints. This second claim can best be brought out in the context of an examination of the idea of computer self-knowledge and self-deception, but the conclusion is perfectly general: the idea that computers are believers, like the idea that computers could have self-knowledge or be self-deceived, is incompatible with the commonsense view. The significance of the argument lies in the choice it forces on us: whether to revise our notion of belief so as to accommodate the claim that computers are believers, or to give up on that claim so as to preserve our pretheoretic notion of the attitudes. We cannot have it both ways
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=316238CI
  http://www.springerlink.com/content/x67006n334r1n383/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=136245&PDF=1
  http://www.springerlink.com/index/X67006N334R1N383.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000004/00136245
  
• Antoni Gomila (1995). From cognitive systems to persons.   (More)
• Larry Hauser (1993). Why isn't my pocket calculator a thinking thing?   (Abstract & more)
  Abstract: My pocket calculator (Cal) has certain arithmetical abilities: it seems Cal calculates. That calculating is thinking seems equally untendentious. Yet these two claims together provide premises for a seemingly valid syllogism whose conclusion -- Cal thinks -- most would deny. I consider several ways to avoid this conclusion, and find them mostly wanting. Either we ourselves can't be said to think or calculate if our calculation-like performances are judged by the standards proposed to rule out Cal; or the standards -- e.g., autonomy and self-consciousness -- make it impossible to verify whether anything or anyone (save myself) meets them. While appeals to the intentionality of thought or the unity of minds provide more credible lines of resistance, available accounts of intentionality and mental unity are insufficiently clear and warranted to provide very substantial arguments against Cal's title to be called a thinking thing. Indeed, considerations favoring granting that title are more formidable than generally appreciated
  Additional links for this entry:
  http://cogprints.org/242/00/wimpcatt.html
  http://members.aol.com/lshauser/wimpcatt.html
  http://cogprints.ecs.soton.ac.uk/archive/00000242/
  http://cogprints.ecs.soton.ac.uk/archive/00000242/00/wimpcatt.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:242
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:242
  http://www.springerlink.com/content/t46235328605618l/fulltext.pdf
  http://www.springerlink.com/content/k5383t2m586208l4/fulltext.pdf
  http://www.springerlink.com/index/T46235328605618L.pdf
  http://www.springerlink.com/index/K5383T2M586208L4.pdf
  http://cogprints.org/242/1/wimpcatt.html
  http://cogprints.org/242/0/wimpcatt.html
  
• Michael Kary & Martin Mahner (2002). How would you know if you synthesized a thinking thing?   (Abstract & more)
  Abstract:   We confront the following popular views: that mind or life are algorithms; that thinking, or more generally any process other than computation, is computation; that anything other than a working brain can have thoughts; that anything other than a biological organism can be alive; that form and function are independent of matter; that sufficiently accurate simulations are just as genuine as the real things they imitate; and that the Turing test is either a necessary or sufficient or scientific procedure for evaluating whether or not an entity is intelligent. Drawing on the distinction between activities and tasks, and the fundamental scientific principles of ontological lawfulness, epistemological realism, and methodological skepticism, we argue for traditional scientific materialism of the emergentist kind in opposition to the functionalism, behaviourism, tacit idealism, and merely decorative materialism of the artificial intelligence and artificial life communities
  Additional links for this entry:
  http://www.springerlink.com/content/h8731k7036822314/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=390424&PDF=1
  http://www.springerlink.com/index/H8731K7036822314.pdf
  http://www.ingentaconnect.com/content/klu/mind/2002/00000012/00000001/00390424
  
• John T. Kearns (1997). Thinking machines: Some fundamental confusions.   (Abstract & more)
  Abstract:   This paper explores Church's Thesis and related claims madeby Turing. Church's Thesis concerns computable numerical functions, whileTuring's claims concern both procedures for manipulating uninterpreted marksand machines that generate the results that these procedures would yield. Itis argued that Turing's claims are true, and that they support (the truth of)Church's Thesis. It is further argued that the truth of Turing's and Church'sTheses has no interesting consequences for human cognition or cognitiveabilities. The Theses don't even mean that computers can do as much as peoplecan when it comes to carrying out effective procedures. For carrying out aprocedure is a purposive, intentional activity. No actual machine does, orcan do, as much
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=322375CI
  http://www.springerlink.com/content/p457647356006413/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=116417&PDF=1
  http://www.springerlink.com/index/P457647356006413.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000002/00116417
  
• Peter Kugel (2002). Computing machines can't be intelligent (...And Turing said so).   (Abstract & more)
  Abstract:   According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don''t believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinelyintelligent, we will have to give them enough initiative (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers more ``initiative'''' can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent
  Additional links for this entry:
  http://www.cs.bc.edu/~kugel/Publications/Hyper.pdf
  http://www.cs.queensu.ca/home/akl/cisc879/papers/PAPERS_FROM_MINDS_AND_MACHINES/VOLUME_12_NO_4/P8T30NK152182338.pdf
  http://www.springerlink.com/content/p8t30nk152182338/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5098145&PDF=1
  http://www.springerlink.com/index/P8T30NK152182338.pdf
  http://www.ingentaconnect.com/content/klu/mind/2002/00000012/00000004/05098145
  
• Jaron Lanier (ms). Mindless thought experiments (a critique of machine intelligence).   (Abstract & more)
  Abstract: Since there isn't a computer that seems conscious at this time, the idea of machine consciousness is supported by thought experiments. Here's one old chestnut: "What if you replaced your neurons one by one with neuron sized and shaped substitutes made of silicon chips that perfectly mimicked the chemical and electric functions of the originals? If you just replaced one single neuron, surely you'd feel the same. As you proceed, as more and more neurons are replaced, you'd stay conscious. Why wouldn't you still be conscious at the end of the process, when you'd reside in a brain shaped glob of silicon? And why couldn't the resulting replacement brain have been manufactured by some other means?"
• Douglas C. Long (1994). Why Machines Can Neither Think nor Feel.   (Abstract & more)
  Abstract: Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings
• John McCarthy (1979). Ascribing mental qualities to machines.   (Abstract & more)
  Abstract: Ascribing mental qualities like beliefs, intentions and wants to a machine is sometimes correct if done conservatively and is sometimes necessary to express what is known about its state. We propose some new definitional tools for this: definitions relative to an approximate theory and second order structural definitions
  Additional links for this entry:
  http://www-formal.stanford.edu/jmc/ascribing.ps
  http://www-formal.stanford.edu/jmc/ascribing.pdf
  http://cogprints.ecs.soton.ac.uk/archive/00000416/00/ascribing.ps
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:416
  http://stinet.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA071423
  
• Marvin L. Minsky (1968). Matter, minds, models.   (More)
• Marvin L. Minsky (1982). Why people think computers can't.   (Abstract & more)
  Abstract: Most people think computers will never be able to think. That is, really think. Not now or ever. To be sure, most people also agree that computers can do many things that a person would have to be thinking to do. Then how could a machine seem to think but not actually think? Well, setting aside the question of what thinking actually is, I think that most of us would answer that by saying that in these cases, what the computer is doing is merely a superficial imitation of human intelligence. It has been designed to obey certain simple commands, and then it has been provided with programs composed of those commands. Because of this, the computer has to obey those commands, but without any idea of what's happening
  Additional links for this entry:
  http://web.media.mit.edu/~minsky/papers/ComputersCantThink.txt
  http://www.cogsci.northwestern.edu/courses/cg207/readings/AIMag03-04-001.pdf
  http://cs.clarku.edu/~jbreecher/public/2005_Can_Computers_Think/Minsky-WhyPeopleThinkComputersCant.pdf
  http://aleph0.clarku.edu/~jbreecher/public/2005_Can_Computers_Think/Minsky-WhyPeopleThinkComputersCant.pdf
  http://act-r.psy.cmu.edu/~douglass/Douglass/Agents/TopicPapers/~TopCandidates~/State-of-AI/AIMag03-04-001.pdf
  
• Bence Nanay (2006). Symmetry between the intentionality of minds and machines? The biological plausibility of Dennett's position.   (Abstract & more)
  Abstract: One of the most influential arguments against the claim that computers can think is that while our intentionality is intrinsic, that of computers is derived: it is parasitic on the intentionality of the programmer who designed the computer-program. Daniel Dennett chose a surprising strategy for arguing against this asymmetry: instead of denying that the intentionality of computers is derived, he endeavours to argue that human intentionality is derived too. I intend to examine that biological plausibility of Dennett’s suggestion and show that Dennett’s argument for the claim that human intentionality is derived because it was designed by natural selection is based on the misunderstanding of how natural selection works
  Additional links for this entry:
  http://www.springerlink.com/content/a44653240255k3g2/fulltext.pdf
  http://www.springerlink.com/index/A44653240255K3G2.pdf
  
• Diane Proudfoot (2004). The implications of an externalist theory of rule-following behavior for robot cognition.   (Abstract & more)
  Abstract:   Given (1) Wittgensteins externalist analysis of the distinction between following a rule and behaving in accordance with a rule, (2) prima facie connections between rule-following and psychological capacities, and (3) pragmatic issues about training, it follows that most, even all, future artificially intelligent computers and robots will not use language, possess concepts, or reason. This argument suggests that AIs traditional aim of building machines with minds, exemplified in current work on cognitive robotics, is in need of substantial revision
  Additional links for this entry:
  http://www.springerlink.com/content/v3gpx4k76m46705k/fulltext.pdf
  http://www.springerlink.com/index/V3GPX4K76M46705K.pdf
  http://www.ingentaconnect.com/content/klu/mind/2004/00000014/00000003/05268464
  
• Zenon W. Pylyshyn (1975). Minds, machines and phenomenology: Some reflections on Dreyfus' What Computers Can't Do.   (More)
• William J. Rapaport (online). Computer processes and virtual persons: Comments on Cole's "artificial intelligence and personal identity".   (Abstract & more)
  Abstract: This is a draft of the written version of comments on a paper by David Cole, presented orally at the American Philosophical Association Central Division meeting in New Orleans, 27 April 1990. Following the written comments are 2 appendices: One contains a letter to Cole updating these comments. The other is the handout from the oral presentation
  Additional links for this entry:
  http://www.cse.buffalo.edu/tech-reports/90-13.ps
  http://www.cse.buffalo.edu/tech-reports/90-13.ps.Z
  http://www.cse.buffalo.edu/~rapaport/Papers/cole.tr.17my90.pdf
  http://www.cse.buffalo.edu/~rapaport/Papers/cole.tr.17my90.pdf
  http://historical.ncstrl.org/litesite-data/suny_buffalo_cs/90-13.ps.Z
  
• Graeme Ritchie (2007). Some empirical criteria for attributing creativity to a computer program.   (Abstract & more)
  Abstract: Over recent decades there has been a growing interest in the question of whether computer programs are capable of genuinely creative activity. Although this notion can be explored as a purely philosophical debate, an alternative perspective is to consider what aspects of the behaviour of a program might be noted or measured in order to arrive at an empirically supported judgement that creativity has occurred. We sketch out, in general abstract terms, what goes on when a potentially creative program is constructed and run, and list some of the relationships (for example, between input and output) which might contribute to a decision about creativity. Specifically, we list a number of criteria which might indicate interesting properties of a program’s behaviour, from the perspective of possible creativity. We go on to review some ways in which these criteria have been applied to actual implementations, and some possible improvements to this way of assessing creativity
  Additional links for this entry:
  http://www.springerlink.com/content/873382581451wk27/fulltext.pdf
  http://www.springerlink.com/index/873382581451WK27.pdf
  http://www.ingentaconnect.com/content/klu/mind/2007/00000017/00000001/00009066
  
• E. Ronald & Moshe Sipper (2001). Intelligence is not enough: On the socialization of talking machines.   (Abstract & more)
  Abstract:   Since the introduction of the imitation game by Turing in 1950 there has been much debate as to its validity in ascertaining machine intelligence. We wish herein to consider a different issue altogether: granted that a computing machine passes the Turing Test, thereby earning the label of ``Turing Chatterbox'', would it then be of any use (to us humans)? From the examination of scenarios, we conclude that when machines begin to participate in social transactions, unresolved issues of trust and responsibility may well overshadow any raw reasoning ability they possess
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=5BJaXSedqfIC&oi=fnd&pg=RA2-PA151&ots=rzjaYNDgnt&sig=3A-d8vteZ5VxcLftqerbTWdpGLI
  http://books.google.com/books?hl=en&lr=&id=ECj6fuEMdp0C&oi=fnd&pg=RA2-PA151&ots=blQIHqmec_&sig=7-0Vb2LD-6m2dlj8qK05keaxOEU
  http://www.springerlink.com/content/l363406154q21317/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=338896&PDF=1
  http://www.springerlink.com/index/L363406154Q21317.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000004/00338896
  
• C. T. A. Schmidt (2005). Of robots and believing.   (Abstract & more)
  Abstract: Discussion about the application of scientific knowledge in robotics in order to build people helpers is widespread. The issue herein addressed is philosophically poignant, that of robots that are “people”. It is currently popular to speak about robots and the image of Man. Behind this lurks the dialogical mind and the questions about the significance of an artificial version of it. Without intending to defend or refute the discourse in favour of ‘recreating’ Man, a lesser familiar question is brought forth: “and what if we were capable of creating a very convincible replica of man (constructing a robot-person), what would the consequences of this be and would we be satisfied with such technology?” Thorny topic; it questions the entire knowledge foundation upon which strong AI/Robotics is positioned. The author argues for improved monitoring of technological progress and thus favours implementing weaker techniques
  Additional links for this entry:
  http://www.springerlink.com/content/h605152238852292/fulltext.pdf
  http://www.springerlink.com/index/H605152238852292.pdf
  http://www.ingentaconnect.com/content/klu/mind/2005/00000015/00000002/00004734
  
• Bernd Carsten Stahl (2006). Responsible computers? A case for ascribing quasi-responsibility to computers independent of personhood or agency.   (Abstract & more)
  Abstract: There has been much debate whether computers can be responsible. This question is usually discussed in terms of personhood and personal characteristics, which a computer may or may not possess. If a computer fulfils the conditions required for agency or personhood, then it can be responsible; otherwise not. This paper suggests a different approach. An analysis of the concept of responsibility shows that it is a social construct of ascription which is only viable in certain social contexts and which serves particular social aims. If this is the main aspect of responsibility then the question whether computers can be responsible no longer hinges on the difficult problem of agency but on the possibly simpler question whether responsibility ascriptions to computers can fulfil social goals. The suggested solution to the question whether computers can be subjects of responsibility is the introduction of a new concept, called “quasi-responsibility” which will emphasise the social aim of responsibility ascription and which can be applied to computers
  Additional links for this entry:
  http://www.cse.dmu.ac.uk/~bstahl/publications/2006_responsible_computers_EIT.pdf
  http://www.springerlink.com/content/27607300gt85x46k/fulltext.pdf
  http://www.springerlink.com/index/27607300GT85X46K.pdf
  http://www.ingentaconnect.com/content/klu/etin/2006/00000008/00000004/00009112
  
• J. David Velleman (online). Artificial agency.   (Abstract & more)
  Abstract: I argue that participants in a virtual world such as "Second Life" exercise genuine agency via their avatars. Indeed, their avatars are fictional bodies with which they act in the virtual world, just as they act in the real world with their physical bodies. Hence their physical bodies can be regarded as their default avatars. I also discuss recent research into "believable" software agents, which are designed on principles borrowed from the character-based arts, especially cinematic animation as practiced by the artists at Disney and Warner Brothers Studios. I claim that these agents exemplify a kind of autonomy that should be of greater interest to philosophers than that exemplified by the generic agent modeled in current philosophical theory. The latter agent is autonomous by virtue of being governed by itself; but a believable agent appears to be governed by a self, which is the anima by which it appears to be animated. Putting these two discussions together, I suggest that philosophers of action should focus their attention on how we animate our bodies
  Additional links for this entry:
  http://phonline.org/paper.php?keynum=807
  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1006884
  
• Mitchell Waldrop (1990). Can computers think?   (More)
• Rodrick Wallace (ms). New mathematical foundations for AI and alife: Are the necessary conditions for animal consciousness sufficient for the design of intelligent machines?   (Abstract & more)
  Abstract: Rodney Brooks' call for 'new mathematics' to revitalize the disciplines of artificial intelligence and artificial life can be answered by adaptation of what Adams has called 'the informational turn in philosophy', aided by the novel perspectives that program gives regarding empirical studies of animal cognition and consciousness. Going backward from the necessary conditions communication theory imposes on animal cognition and consciousness to sufficient conditions for machine design is, however, an extraordinarily difficult engineering task. The most likely use of the first generations of conscious machines will be to model the various forms of psychopathology, since we have little or no understanding of how consciousness is stabilized in humans or other animals
  Additional links for this entry:
  http://philsci-archive.pitt.edu/archive/00002646/01/alife.pdf
  
• Darren Whobrey (2001). Machine mentality and the nature of the ground relation.   (Abstract & more)
  Abstract:   John Searle distinguished between weak and strong artificial intelligence (AI). This essay discusses a third alternative, mild AI, according to which a machine may be capable of possessing a species of mentality. Using James Fetzer's conception of minds as semiotic systems, the possibility of what might be called ``mild AI'' receives consideration. Fetzer argues against strong AI by contending that digital machines lack the ground relationship required of semiotic systems. In this essay, the implementational nature of semiotic processes posited by Charles S. Peirce's triadic sign relation is re-examined in terms of the underlying dispositional processes and the ontological levels they would span in an inanimate machine. This suggests that, if non-human mentality can be replicated rather than merely simulated in a digital machine, the direction to pursue appears to be that of mild AI
  Additional links for this entry:
  http://www.mildai.org/papers/MAM2000-0-DJRW.pdf
  http://www.springerlink.com/content/tm143v51k2638028/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=323262&PDF=1
  http://www.springerlink.com/index/TM143V51K2638028.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000003/00323262
  

Computation and Representation

Symbols and Symbol Systems

• C. Franklin Boyle (2001). Transduction and degree of grounding.   (Abstract & more)
  Abstract: While I agree in general with Stevan Harnad's symbol grounding proposal, I do not believe "transduction" (or "analog process") PER SE is useful in distinguishing between what might best be described as different "degrees" of grounding and, hence, for determining whether a particular system might be capable of cognition. By 'degrees of grounding' I mean whether the effects of grounding go "all the way through" or not. Why is transduction limited in this regard? Because transduction is a physical process which does not speak to the issue of representation, and, therefore, does not explain HOW the informational aspects of signals impinging on sensory surfaces become embodied as symbols or HOW those symbols subsequently cause behavior, both of which, I believe, are important to grounding and to a system's cognitive capacity. Immunity to Searle's Chinese Room (CR) argument does not ensure that a particular system is cognitive, and whether or not a particular degree of groundedness enables a system to pass the Total Turing Test (TTT) may never be determined
  Additional links for this entry:
  http://cogsci.soton.ac.uk/~harnad/Temp/Think/boyle.htm
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.boyle.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.boyle.html
  
• Selmer Bringsjord (online). People are infinitary symbol systems: No sensorimotor capacity necessary.   (Abstract & more)
  Abstract: Stevan Harnad and I seem to be thinking about many of the same issues. Sometimes we agree, sometimes we don't; but I always find his reasoning refreshing, his positions sensible, and the problems with which he's concerned to be of central importance to cognitive science. His "Grounding Symbols in the Analog World with Neural Nets" (= GS) is no exception. And GS not only exemplifies Harnad's virtues, it also provides a springboard for diving into Harnad- Bringsjord terrain
  Additional links for this entry:
  http://psycprints.ecs.soton.ac.uk/archive/00000167/
  http://cogsci.soton.ac.uk/~harnad/Temp/Think/bringsj.htm
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.bringsjord.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.bringsjord.html
  http://psycprints.ecs.soton.ac.uk/archive/00000167/02/psyc.01.12.038.symbolism-connectionism.5.bringsjord
  http://psycprints.ecs.soton.ac.uk/archive/00000167/01/psyc.01.12.038.symbolism-connectionism.5.bringsjord.xml
  
• Andy Clark (2006). Material symbols.   (Abstract & more)
  Abstract: What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). In the present treatment I explore a third option, which I shall call the "complementarity" view of language. According to this third view the actual symbol structures of a given language add cognitive value by complementing (without being replicated by) the more basic modes of operation and representation endemic to the biological brain. The "cognitive bonus" that language brings is, on this model, not to be cashed out either via the ultimately mysterious notion of "thinking in a given natural language" or via some process of exhaustive translation into another inner code. Instead, we should try to think in terms of a kind of coordination dynamics in which the forms and structures of a language qua material symbol system play a key and irreducible role. Understanding language as a complementary cognitive resource is, I argue, an important part of the much larger project (sometimes glossed in terms of the "extended mind") of understanding human cognition as essentially and multiply hybrid: as involving a complex interplay between internal biological resources and external non-biological resources
  Additional links for this entry:
  http://www.psych.unito.it/csc/cogsci05/frame/talk/plen1-clark.pdf
  http://data.cstr.ed.ac.uk/internal/library/proceedings/2005/cogsci2005/docs/p1.pdf
  http://www.informaworld.com/smpp/./ftinterface~content=a747697830~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/XNRG5M77H65766ML.pdf
  http://www.informaworld.com/index/747697830.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2006/00000019/00000003/art00002
  
• Stevan Harnad (1992). Connecting object to symbol in modeling cognition.   (Abstract & more)
  Abstract: Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism (Dietrich 1990, Fodor 1975, Harnad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is nonsymbolic (Fodor & Pylyshyn 1988, or, as some have hopefully dubbed it, "subsymbolic" Smolensky 1988). This paper will examine what is and is not a symbol system. A hybrid nonsymbolic/symbolic system will be sketched in which the meanings of the symbols are grounded bottom-up in the system's capacity to discriminate and identify the objects they refer to. Neural nets are one possible mechanism for learning the invariants in the analog sensory projection on which successful categorization is based. "Categorical perception" (Harnad 1987a), in which similarity space is "warped" in the service of categorization, turns out to be exhibited by both people and nets, and may mediate the constraints exerted by the analog world of objects on the formal world of symbols
  Additional links for this entry:
  http://eprints.ecs.soton.ac.uk/3374/
  http://eprints.resist.ecs.soton.ac.uk/3374/
  http://cogprints.ecs.soton.ac.uk/archive/00001583/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/83/
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad92.symbol.object.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad92.symbol.object.html
  http://eprints.ecs.soton.ac.uk/archive/00003374/02/harnad92.symbol.object.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:3374
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1583
  http://cogprints.org/1583/1/harnad92.symbol.object.html
  http://cogprints.org/1583/0/harnad92.symbol.object.html
  
• Stevan Harnad (2002). Symbol grounding and the origin of language.   (Abstract & more)
  Abstract: What language allows us to do is to "steal" categories quickly and effortlessly through hearsay instead of having to earn them the hard way, through risky and time-consuming sensorimotor "toil" (trial-and-error learning, guided by corrective feedback from the consequences of miscategorisation). To make such linguistic "theft" possible, however, some, at least, of the denoting symbols of language must first be grounded in categories that have been earned through sensorimotor toil (or else in categories that have already been "prepared" for us through Darwinian theft by the genes of our ancestors); it cannot be linguistic theft all the way down. The symbols that denote categories must be grounded in the capacity to sort, label and interact with the proximal sensorimotor projections of their distal category-members in a way that coheres systematically with their semantic interpretations, both for individual symbols, and for symbols strung together to express truth-value-bearing propositions
  Additional links for this entry:
  http://eprints.resist.ecs.soton.ac.uk/7714/
  http://eprints.resist.ecs.soton.ac.uk/6471/
  http://bi.snu.ac.kr/Courses/4ai05s/Essay/2-06.pdf
  http://cogprints.ecs.soton.ac.uk/archive/00002133/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/21/33/
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad02.symlang.htm
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad02.symlang.htm
  http://eprints.ecs.soton.ac.uk/archive/00007714/01/harnad02.symlang.htm
  http://eprints.ecs.soton.ac.uk/archive/00006471/01/harnad02.symlang.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:eprints.ecs.soton.ac.uk:7714
  
• Stevan Harnad (ms). Symbol grounding is an empirical problem: Neural nets are just a candidate component.   (Abstract & more)
  Abstract: "Symbol Grounding" is beginning to mean too many things to too many people. My own construal has always been simple: Cognition cannot be just computation, because computation is just the systematically interpretable manipulation of meaningless symbols, whereas the meanings of my thoughts don't depend on their interpretability or interpretation by someone else. On pain of infinite regress, then, symbol meanings must be grounded in something other than just their interpretability if they are to be candidates for what is going on in our heads. Neural nets may be one way to ground the names of concrete objects and events in the capacity to categorize them (by learning the invariants in their sensorimotor projections). These grounded elementary symbols could then be combined into symbol strings expressing propositions about more abstract categories. Grounding does not equal meaning, however, and does not solve any philosophical problems
  Additional links for this entry:
  http://eprints.resist.ecs.soton.ac.uk/3367/
  http://eprints.ecs.soton.ac.uk/archive/00003367/
  http://cogprints.org/1588/0/harnad93.cogsci.html
  http://cogprints.ecs.soton.ac.uk/archive/00001588/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/88/
  http://eprints.ecs.soton.ac.uk/3367/01/harnad93.cogsci.html
  http://cogprints.ecs.soton.ac.uk/documents/disk0/00/00/15/88/
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.cogsci.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.cogsci.html
  http://cogprints.ecs.soton.ac.uk/archive/00001588/00/harnad93.cogsci.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:3367
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1588
  http://cogprints.org/1588/1/harnad93.cogsci.html
  
• Stevan Harnad (1990). The symbol grounding problem.   (Abstract & more)
  Abstract: There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem: How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and (2) categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g., An X is a Y that is Z). Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded
  Additional links for this entry:
  http://arxiv.org/abs/cs.AI/9906002
  http://eprints.resist.ecs.soton.ac.uk/8175/
  http://portal.acm.org/citation.cfm?id=96679
  http://cogprints.org/3106/1/sgproblem1.html
  http://citeseer.ist.psu.edu/harnad90symbol.html
  http://eprints.ecs.soton.ac.uk/archive/00008175/
  http://adsabs.harvard.edu/abs/1999cs........6002H
  http://www.ecs.soton.ac.uk/~harnad/Temp/symgro.htm
  http://cogprints.ecs.soton.ac.uk/archive/00003106/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/06/15/
  http://cogprints.org/615/00/The_Symbol_Grounding_Problem.html
  http://www.isrl.uiuc.edu/~amag/langev/paper/harnad90theSymbol.html
  http://cogprints.ecs.soton.ac.uk/archive/00003106/01/sgproblem1.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad90.sgproblem.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad90.sgproblem.html
  http://wexler.free.fr/library/files/harnad (1990) the symbol grounding problem.pdf
  http://cogprints.ecs.soton.ac.uk/archive/00000615/00/The_Symbol_Grounding_Problem.html
  http://www.ecs.soton.ac.uk/%7Eharnad/Papers/Harnad/harnad90.sgproblem.html
  http://cogprints.org/615/1/The_Symbol_Grounding_Problem.html
  http://cogprints.org/615/0/The_Symbol_Grounding_Problem.html
  
• Karl F. MacDorman (1997). How to ground symbols adaptively.   (More)
• Allen Newell & Herbert A. Simon (1981). Computer science as empirical inquiry: Symbols and search.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=1283930
  http://www.jdl.ac.cn/turing/pdf/p113-newell.pdf
  http://portal.acm.org/citation.cfm?id=216000.216007
  http://home.dei.polimi.it/colombet/IA_2004/NewellandSimon.pdf
  http://www.elet.polimi.it/upload/colombet/IA_2004/NewellandSimon.pdf
  http://act-r.psy.cmu.edu/~douglass/Douglass/Agents/TopicPapers/PSSH/PSSH1.pdf
  http://act-r.psy.cmu.edu/people/douglass/Douglass/Agents/TopicPapers/PSSH/PSSH1.pdf
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E4xI7EhGQs&sig=JepEkCn93darsdPpvcGEn7VG61E
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oJfHgzVu&sig=5jb8G10Jz9vcHSA8SMjvAyDa79E
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E4xIcIdzXt&sig=x8TqXd03sMF7Ku2tjTjKPLStp7M
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oKbHjCZv&sig=G4uV6cXYV2x1beYCw6nX6T4sbdI
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oIdGczZr&sig=-ndDGHatiVA3bw4jwnF9OuPsYOE
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oJ9HbFUs&sig=bj8ATdZXgK2geid62HIRWyOdnuo
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oFdDiBTr&sig=aHVuO1cvjfMeqsc-mx75yheObrA
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oF7DeyQy&sig=cEaNBtdTsZioMpkk6W2Yd7_Ecf8
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oI7FhAWv&sig=TORphnvN5dYkzgzVFVGRZUaWdJc
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oEbCjyVy&sig=NrAngS_FECgzGE2Iwqo6KD9OnDU
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E4xIaIczSt&sig=YUwqrPXzwnaBIYgYiu0RVv1K52I
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oHbFcCWw&sig=0Avl1AKiXUbrSFEGnL7r0e8A8bM
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oG9EdDXt&sig=43O3YB2BeYIwnOoSeJ-LE-V8lao
  http://books.google.com/books?hl=en&lr=&id=UqhjMVwYKJsC&oi=fnd&pg=PA109&ots=E5oGfEiFSy&sig=uTVh7cE69OpNBo9FxMib124gZ00
  
• Allen Newell (1980). Physical symbol systems.   (More)
  Additional links for this entry:
  http://www.leaonline.com/doi/pdf/10.1207/s15516709cog0402_2
  http://www.leaonline.com/doi/pdfplus/10.1207/s15516709cog0402_2
  http://www.cogsci.rpi.edu/CSJarchive/1980v04/i02/p0135p0183/MAIN.PDF
  http://stinet.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA224247
  
• Steven Pinker (2004). Why nature & nurture won't go away.   (More)
  Additional links for this entry:
  http://www.mitpressjournals.org/doi/abs/10.1162/0011526042365591
  http://pinker.wjh.harvard.edu/articles/papers/nature_nurture.pdf
  http://pinker.wjh.harvard.edu/articles/papers/nature_nurture.pdf
  http://mitpress.mit.edu/catalog/item/default.asp?tid=17270&ttype=6
  http://mitpress.mit.edu/catalog/item/default.asp?ttype=6&tid=17270
  http://www.mitpressjournals.org/doi/pdfplus/10.1162/0011526042365591
  http://www.ingentaconnect.com/content/mitpress/dae/2004/00000133/00000004/art00001
  
• Herbert L. Roitblat (2001). Computational grounding.   (Abstract & more)
  Abstract: Harnad defines computation to mean the manipulation of physical symbol tokens on the basis of syntactic rules defined over the shapes of the symbols, independent of what, if anything, those symbols represent. He is, of course, free to define terms in any way that he chooses, and he is very clear about what he means by computation, but I am uncomfortable with this definition. It excludes, at least at a functional level of description, much of what a computer is actually used for, and much of what the brain/mind does. When I toss a Frisbee to the neighbor's dog, the dog does not, I think, engage in a symbolic soliloquy about the trajectory of the disc, the wind's effects on it, and formulas for including lift and the acceleration due to gravity. There are symbolic formulas for each of these relations, but the dog insofar as I can tell, does not use any of these formulas. Nevertheless, it computes these factors in order to intercept the disc in the air. I argue that determining the solution to a differential equation is at least as much computation as is processing symbols. The disagreement is over what counts as computation, I think that Harnad and I both agree that the dog solves the trajectory problem implicitly. This definition is important, because, although Harnad offers a technical definition for what he means by computation, the folk- definition of the term is probably interpreted differently, and I believe this leads to trouble
  Additional links for this entry:
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.roitblat.html
  
• Susan Schneider (2009). Lot, ctm, and the elephant in the room.   (Abstract & more)
  Abstract: According to the language of thought (LOT) approach and the related computational theory of mind (CTM), thinking is the processing of symbols in an inner mental language that is distinct from any public language. Herein, I explore a deep problem at the heart of the LOT/CTM program—it has yet to provide a plausible conception of a mental symbol
  Additional links for this entry:
  http://www.sas.upenn.edu/~sls/SchneiderLOTSynthesesent.rtf
  http://www.sas.upenn.edu/~sls/documents/SchneiderSyntheseLOT.pdf
  http://www.springerlink.com/content/f9312g2m003630r0/fulltext.pdf
  
• Susan Schneider (forthcoming). The nature of primitive symbols in the language of thought.   (Abstract & more)
  Abstract: This paper provides a theory of the nature of symbols in the language of thought (LOT). My discussion consists in three parts. In part one, I provide three arguments for the individuation of primitive symbols in terms of total computational role. The first of these arguments claims that Classicism requires that primitive symbols be typed in this manner; no other theory of typing will suffice. The second argument contends that without this manner of symbol individuation, there will be computational processes that fail to supervene on syntax, together with the rules of composition and the computational algorithms. The third argument says that cognitive science needs a natural kind that is typed by total computational role. Otherwise, either cognitive science will be incomplete, or its laws will have counterexamples. Then, part two defends this view from a criticism, offered by both Jerry Fodor and Jesse Prinz, who respond to my view with the charge that because the types themselves are individuated
  Additional links for this entry:
  http://www.sas.upenn.edu/~sls/documents/SchneiderNatureSymbolsMindLang.doc
  http://www.sas.upenn.edu/~sls/documents/SchneiderNatureSymbolsMindLang-1-2.pdf
  http://www.sas.upenn.edu/~sls/documents/Schneider-MindLangSymbols.pdf
  
• Ron Sun (2000). Symbol grounding: A new look at an old idea.   (Abstract & more)
  Abstract: Symbols should be grounded, as has been argued before. But we insist that they should be grounded not only in subsymbolic activities, but also in the interaction between the agent and the world. The point is that concepts are not formed in isolation (from the world), in abstraction, or "objectively." They are formed in relation to the experience of agents, through their perceptual/motor apparatuses, in their world and linked to their goals and actions. This paper takes a detailed look at this relatively old issue, with a new perspective, aided by our work of computational cognitive model development. To further our understanding, we also go back in time to link up with earlier philosophical theories related to this issue. The result is an account that extends from computational mechanisms to philosophical abstractions
  Additional links for this entry:
  http://www.cogsci.rpi.edu/~rsun/sun.PP00.ps
  http://citeseer.ist.psu.edu/sun99symbol.html
  http://www.informaworld.com/smpp/./ftinterface~content=a713690432~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/KKLQAQTYVG4RUY7G.pdf
  http://www.informaworld.com/index/KKLQAQTYVG4RUY7G.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2000/00000013/00000002/art00001
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a713690432~fulltext=713240930
  
• Mariarosaria Taddeo & Luciano Floridi (2008). A praxical solution of the symbol grounding problem.   (Abstract & more)
  Abstract: This article is the second step in our research into the Symbol Grounding Problem (SGP). In a previous work, we defined the main condition that must be satisfied by any strategy in order to provide a valid solution to the SGP, namely the zero semantic commitment condition (Z condition). We then showed that all the main strategies proposed so far fail to satisfy the Z condition, although they provide several important lessons to be followed by any new proposal. Here, we develop a new solution of the SGP. It is called praxical in order to stress the key role played by the interactions between the agents and their environment. It is based on a new theory of meaning—Action-based Semantics (AbS)—and on a new kind of artificial agents, called two-machine artificial agents (AM²). Thanks to their architecture, AM2s implement AbS, and this allows them to ground their symbols semantically and to develop some fairly advanced semantic abilities, including the development of semantically grounded communication and the elaboration of representations, while still respecting the Z condition
  Additional links for this entry:
  http://www.springerlink.com/content/f748ru5636784058/fulltext.pdf
  
• Evan Thompson (1997). Symbol grounding: A bridge from artificial life to artificial intelligence.   (Abstract & more)
  Abstract: This paper develops a bridge from AL issues about the symbol–matter relation to AI issues about symbol-grounding by focusing on the concepts of formality and syntactic interpretability. Using the DNA triplet-amino acid specification relation as a paradigm, it is argued that syntactic properties can be grounded as high-level features of the non-syntactic interactions in a physical dynamical system. This argu- ment provides the basis for a rebuttal of John Searle’s recent assertion that syntax is observer-relative (1990, 1992). But the argument as developed also challenges the classic symbol-processing theory of mind against which Searle is arguing, as well as the strong AL thesis that life is realizable in a purely computational medium. Finally, it provides a new line of support for the autonomous systems approach in AL and AI (Varela & Bourgine 1992a, 1992b). © 1997 Academic Press
  Additional links for this entry:
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9209755&dopt=Citation
  http://www.ingentaconnect.com/content/ap/br/1997/00000034/00000001/art00906
  

Computational Semantics

• Varol Akman (1998). Situations and artificial intelligence.   (More)
• Radu J. Bogdan (1994). By way of means and ends.   (Abstract & more)
  Abstract: This chapter provides the teleological foundations for our analysis of guidance to goal. Its objective is to ground goal-directedness genetically. The basic suggestion is this. Organisms are small things, with few energy resources and puny physical means, battling a ruthless physical and biological nature. How do they manage to survive and multiply? CLEVERLY, BY ORGANIZING
• Jakub Szymanik & Marcin Zajenkowski (2009). Comprehension of Simple Quantifiers. Empirical Evaluation of a Computational Model.   (Abstract & more)
  Abstract: We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.
  In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides
  evidence
• Daniel C. Dennett (2003). The Baldwin Effect: A Crane, Not a Skyhook.   (Abstract & more)
  Abstract: In 1991, I included a brief discussion of the Baldwin effect in my account of the evolution of human consciousness, thinking I was introducing to non-specialist readers a little-appreciated, but no longer controversial, wrinkle in orthodox neo-Darwinism. I had thought that Hinton and Nowlan (1987) and Maynard Smith (1987) had shown clearly and succinctly how and why it worked, and restored the neglected concept to grace. Here is how I put it then
  Additional links for this entry:
  http://ase.tufts.edu/cogstud/papers/baldwincranefin.htm
  http://books.google.com/books?hl=en&lr=&id=yBtRzBilw1MC&oi=fnd&pg=PA69&dq=The+Baldwin+effect+Dennett&ots=2I8w0wd3n_&sig=urmY-aQvjQsgpb_W81dI6SjJvGc
  
• Jerry A. Fodor (1978). Tom swift and his procedural grandmother.   (More)
• Stevan Harnad (2002). Darwin, Skinner, Turing and the mind.   (Abstract & more)
  Abstract: Darwin differs from Newton and Einstein in that his ideas do not require a complicated or deep mind to understand them, and perhaps did not even require such a mind in order to generate them in the first place. It can be explained to any school-child (as Newtonian mechanics and Einsteinian relativity cannot) that living creatures are just Darwinian survival/reproduction machines. They have whatever structure they have through a combination of chance and its consequences: Chance causes changes in the genetic blueprint from which organisms' bodies are built, and if those changes are more successful in helping their owners survive and reproduce than their predecessors or their rivals, then, by definition, those changes are reproduced, and thereby become more prevalent in succeeding generations: Whatever survives/reproduces better survives/reproduces better. That is the tautological force that shaped us
  Additional links for this entry:
  http://cogprints.org/3016/01/darwin.htm
  http://eprints.resist.ecs.soton.ac.uk/7715/
  http://eprints.ecs.soton.ac.uk/7715/01/darwin.htm
  http://cogprints.ecs.soton.ac.uk/archive/00003016/
  http://www.ecs.soton.ac.uk/~harnad/Temp/darwin.htm
  http://eprints.ecs.soton.ac.uk/7715/01/darwin.htm
  http://www.ecs.soton.ac.uk/~harnad/Temp/darwin.htm
  http://cogprints.ecs.soton.ac.uk/archive/00003016/
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:3016
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:3016
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:7715
  
• Philip N. Johnson-Laird (1977). Procedural semantics.   (More)
• Drew McDermott (1978). Tarskian semantics, or no notation without denotation.   (More)
  Additional links for this entry:
  http://www.leaonline.com/doi/abs/10.1207/s15516709cog0203_5
  http://www.leaonline.com/doi/pdf/10.1207/s15516709cog0203_5
  http://www.leaonline.com/doi/pdfplus/10.1207/s15516709cog0203_5
  
• David Papineau (2006). The cultural origins of cognitive adaptations.   (Abstract & more)
  Abstract: According to an influential view in contemporary cognitive science, many human cognitive capacities are innate. The primary support for this view comes from ‘poverty of stimulus’ arguments. In general outline, such arguments contrast the meagre informational input to cognitive development with its rich informational output. Consider the ease with which humans acquire languages, become facile at attributing psychological states (‘folk psychology’), gain knowledge of biological kinds (‘folk biology’), or come to understand basic physical processes (‘folk physics’). In all these cases, the evidence available to a growing child is far too thin and noisy for it to be plausible that the underlying principles involved are derived from general learning mechanisms. This only alternative hypothesis seems to be that the child’s grasp of these principles is innate. (Cf. Laurence and Margolis, 2001.)
  Additional links for this entry:
  http://journals.cambridge.org/abstract_S1358246105056134
  
• William J. Rapaport (1995). Understanding understanding: Syntactic semantics and computational cognition.   (More)
  Additional links for this entry:
  http://www.cse.buffalo.edu/~rapaport/Papers/understanding.ps
  http://historical.ncstrl.org/litesite-data/suny_buffalo_cs/94-28.ps.Z
  http://links.jstor.org/sici?sici=1520-8583(1995)9<49:UUSSAC>2.0.CO;2-G
  http://www.jstor.org/sici?sici=1520-8583(1995)9<49:UUSSAC>2.0.CO;2-G
  http://www.jstor.org/stable/pdfplus/2214212.pdf
  
• B. Smith (1988). On the semantics of clocks.   (More)
• Jakub Szymanik & Marcin Zajenkowski (2009). Understanding Quantifiers in Language.   (Abstract & more)
  Abstract: We compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and pushdown automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.
• Erkan Tin & Varol Akman (1994). Computational situation theory.   (Abstract & more)
  Abstract: Situation theory has been developed over the last decade and various versions of the theory have been applied to a number of linguistic issues. However, not much work has been done in regard to its computational aspects. In this paper, we review the existing approaches towards `computational situation theory' with considerable emphasis on our own research
  Additional links for this entry:
  http://citeseer.ist.psu.edu/599996.html
  http://cogprints.ecs.soton.ac.uk/archive/00000200/
  http://portal.acm.org/citation.cfm?id=191604.191608
  http://citeseer.ist.psu.edu/tin94computational.html
  http://www.cs.bilkent.edu.tr/~akman/jour-papers/sigart/sigart1994.pdf
  http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:200
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:200
  http://cogprints.org/200/2/sig.ps
  http://cogprints.org/200/0/sig.ps
  
• W. Woods (1981). Procedural semantics as a theory of meaning.   (More)

Implicit/Explicit Rules and Representations

• William P. Bechtel (forthcoming). Explanation: Mechanism, modularity, and situated cognition.   (Abstract & more)
  Abstract: The situated cognition movement has emerged in recent decades (although it has roots in psychologists working earlier in the 20^th century including Vygotsky, Bartlett, and Dewey) largely in reaction to an approach to explaining cognition that tended to ignore the context in which cognitive activities typically occur. Fodor’s (1980) account of the research strategy of methodological solipsism, according to which only representational states within the mind are viewed as playing causal roles in producing cognitive activity, is an extreme characterization of this approach. (As Keith Gunderson memorably commented when Fodor first presented this characterization, it amounts to reversing behaviorism by construing the mind as a white box in a black world). Critics as far back as the 1970s and 1980s objected to many experimental paradigms in cognitive psychology as not being ecologically valid; that is, they maintained that the findings only applied to the artificial circumstances created in the laboratory and did not generalize to real world settings (Neisser, 1976; 1987). The situated cognition movement, however, goes much further than demanding ecologically valid experiments—it insists that an agent’s cognitive activities are inherently embedded and supported by dynamic interactions with the agent’s body and features of its environment
• Andy Clark (1991). In defense of explicit rules.   (More)
• Daniel C. Dennett (1993). Review of F. Varela, E. Thompson and E. Rosch, The Embodied Mind.   (Abstract & more)
  Abstract: Cognitive science, as an interdisciplinary school of thought, may have recently moved beyond the bandwagon stage onto the throne of orthodoxy, but it does not make a favorable first impression on many people. Familiar reactions on first encounters range from revulsion to condescending dismissal--very few faces in the crowd light up with the sense of "Aha! So that's how the mind works! Of course!" Cognitive science leaves something out, it seems; moreover, what it apparently leaves out is important, even precious. Boiled down to its essence, cognitive science proclaims that in one way or another our minds are computers, and this seems so mechanistic, reductionistic, intellectualistic, dry, philistine, unbiological. It leaves out emotion, or what philosophers call qualia, or value, or mattering, or . . . the soul. It doesn't explain what minds are so much as attempt to explain minds away
  Additional links for this entry:
  http://ase.tufts.edu/cogstud/papers/varela.htm
  http://cogprints.ecs.soton.ac.uk/archive/00000273/
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:273
  
• Nagarjuna G. (2009). Collaborative creation of teaching-learning sequences and an Atlas of knowledge.   (Abstract & more)
  Abstract: Our focus in the article is to introduce a simple methodology of generating teaching-learning sequences using the semantic network techinque, followed by the emergent properties of such a network and their implications for the teaching-learning process (didactics) with marginal notes on epistemological implications. A collaborative portal for teachers, which publishes a network of prerequisites for teaching/learning any concept or an activity is introduced. The article ends with an appeal to the global community to contribute prerequisites of any subject to complete the global roadmap for an altas being built on similar lines as Wikipedia. The portal is launched and waiting for community participation at http://www.gnowledge.org.
  Additional links for this entry:
  http://cogprints.org/6588/1/collaborative-LTS-wpversion-preprint.pdf
  http://cogprints.org/6588/1/collaborative%2DLTS%2Dwpversion%2Dpreprint.pdf
  
• Robert F. Hadley (1995). The 'explicit-implicit' distinction.   (Abstract & more)
  Abstract:   Much of traditional AI exemplifies the explicit representation paradigm, and during the late 1980''s a heated debate arose between the classical and connectionist camps as to whether beliefs and rules receive an explicit or implicit representation in human cognition. In a recent paper, Kirsh (1990) questions the coherence of the fundamental distinction underlying this debate. He argues that our basic intuitions concerning explicit and implicit representations are not only confused but inconsistent. Ultimately, Kirsh proposes a new formulation of the distinction, based upon the criterion ofconstant time processing.The present paper examines Kirsh''s claims. It is argued that Kirsh fails to demonstrate that our usage of explicit and implicit is seriously confused or inconsistent. Furthermore, it is argued that Kirsh''s new formulation of the explicit-implicit distinction is excessively stringent, in that it banishes virtually all sentences of natural language from the realm of explicit representation. By contrast, the present paper proposes definitions for explicit and implicit which preserve most of our strong intuitions concerning straightforward uses of these terms. It is also argued that the distinction delineated here sustains the meaningfulness of the abovementioned debate between classicists and connectionists
  Additional links for this entry:
  http://www.springerlink.com/content/content/kl736k107182317h/fulltext.pdf
  http://www.springerlink.com/content/kl736k107182317h/fulltext.pdf
  http://www.springerlink.com/index/KL736K107182317H.pdf
  
• Fernando Martínez & Jesús Ezquerro Martínez (1998). Explicitness with psychological ground.   (Abstract & more)
  Abstract:   Explicitness has usually been approached from two points of view, labelled by Kirsh the structural and the process view, that hold opposite assumptions to determine when information is explicit. In this paper, we offer an intermediate view that retains intuitions from both of them. We establish three conditions for explicit information that preserve a structural requirement, and a notion of explicitness as a continuous dimension. A problem with the former accounts was their disconnection with psychological work on the issue. We review studies by Karmiloff-Smith, and Shanks and St. John to show that the proposed conditions have psychological grounds. Finally, we examine the problem of explicit rules in connectionist systems in the light of our framework
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=381463CI
  http://www.springerlink.com/content/j4u0011h11660267/fulltext.pdf
  http://www.springerlink.com/index/J4U0011H11660267.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000008/00000003/00160303
  
• Lawrence A. Shapiro (ms). The embodied cognition research program.   (Abstract & more)
  Abstract: Unifying traditional cognitive science is the idea that thinking is a process of symbol manipulation, where symbols lead both a syntactic and a semantic life. The syntax of a symbol comprises those properties in virtue of which the symbol undergoes rule-dictated transformations. The semantics of a symbol constitute the symbolsÕ meaning or representational content. Thought consists in the syntactically determined manipulation of symbols, but in a way that respects their semantics. Thus, for instance, a calculating computer sensitive only to the shape of symbols might produce the symbol Ô5Õ in response to the inputs Ô2Õ, Ô+Õ, and Ô3Õ. As far as the computer is concerned, these symbols have no meaning, but because of its program it will produce outputs that, to the user, Òmake senseÓ given the meanings the user attributes to the symbols
  Additional links for this entry:
  http://philosophy.wisc.edu/shapiro/HomePage/PhilComp.htm
  http://philosophy.wisc.edu/shapiro/HomePage/embodiedcognition.pdf
  
• Paul G. Skokowski (1994). Can computers carry content "inexplicitly"?   (Abstract & more)
  Abstract:   I examine whether it is possible for content relevant to a computer''s behavior to be carried without an explicit internal representation. I consider three approaches. First, an example of a chess playing computer carrying emergent content is offered from Dennett. Next I examine Cummins response to this example. Cummins says Dennett''s computer executes a rule which is inexplicitly represented. Cummins describes a process wherein a computer interprets explicit rules in its program, implements them to form a chess-playing device, then this device executes the rules in a way that exhibits them inexplicitly. Though this approach is intriguing, I argue that the chess-playing device cannot exist as imagined. The processes of interpretation and implementation produce explicit representations of the content claimed to be inexplicit. Finally, the Chinese Room argument is examined and shown not to save the notion of inexplicit information. This means the strategy of attributing inexplicit content to a computer which is executing a rule, fails
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=0148099CI
  http://www.springerlink.com/content/j821320368ht5863/fulltext.pdf
  http://www.springerlink.com/index/J821320368HT5863.pdf
  
• Peter Slezak (1999). Situated cognition.   (Abstract & more)
  Abstract: The self-advertising, at least, suggests that 'situated cognition' involves the most fundamental conceptual re-organization in AI and cognitive science, even appearing to deny that cognition is to be explained by mental representations. In their defence of the orthodox symbolic representational theory, A. Vera and H. Simon (1993) have rebutted many of these claims, but they overlook an important reading of situated arguments which may, after all, involve a revolutionary insight. I show that the whole debate turns on puzzles familiar from the history of philosophy and psychology and these may serve to clarify the current disputes
• John Sutton (2000). The body and the brain.   (Abstract & more)
  Abstract: Does self?knowledge help? A rationalist, presumably, thinks that it does: both that self?knowledge is possible and that, if gained through appropriate channels, it is desirable. Descartes notoriously claimed that, with appropriate methods of enquiry, each of his readers could become an expert on herself or himself. As well as the direct, first?person knowledge of self to which we are led in the Meditationes , we can also seek knowledge of our own bodies, and of the union of our minds and our bodies: the latter forms of self?knowledge are inevitably imperfect, but are no less important in guiding our conduct in the search after truth

AI without Representation?

• Kristin Andrews (web). Critter psychology: On the possibility of nonhuman animal folk psychology.   (Abstract & more)
  Abstract: Humans have a folk psychology, without question. Paul Churchland used the term to describe “our commonsense conception of psychological phenomena” (Churchland 1981, p. 67), whatever that may be. When we ask the question whether animals have their own folk psychology, we’re asking whether any other species has a commonsense conception of psychological phenomenon as well. Different versions of this question have been discussed over the past 25 years, but no clear answer has emerged. Perhaps one reason for this lack of progress is that we don’t clearly understand the question. In asking whether animals have folk psychology, I hope to help clarify the concept of folk psychology itself, and in the process, to gain a greater understanding of the role of belief and desire attribution in human social interaction
  Additional links for this entry:
  http://www.springerlink.com/index/w2506611815711g1.pdf
  
• Rodney Brooks (1991). Intelligence without representation.   (Abstract & more)
  Abstract: Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments
  Additional links for this entry:
  http://people.csail.mit.edu/brooks/papers/representation.pdf
  http://people.csail.mit.edu/~brooks/papers/representation.pdf
  http://www.ai.mit.edu/people/brooks/papers/representation.pdf
  http://www.cs.iastate.edu/~honavar/Courses/cs673/spring00/brooks2.pdf
  http://www.valentiniweb.com/Piermo/robotica/doc/Brooks/representation.pdf
  http://www.scs.ryerson.ca/~aferworn/courses/CPS607/CLASSES/subsumption.pdf
  http://hugo.csie.ntu.edu.tw/~yjhsu/courses/u1760/Online/papers/brooks2.pdf
  http://agents.csie.ntu.edu.tw/~yjhsu/courses/u1760/Online/papers/brooks2.pdf
  http://www.ee.pdx.edu/~mperkows/ML_LAB/Giant_Hexapod/brooks.representation.pdf
  http://www.inf.ufrgs.br/~alvares/CMP124SMA/IntelligenceWithoutRepresentation.pdf
  http://courses.media.mit.edu/2004spring/mas966/Brooks Int wo rep (orig version).pdf
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2506361CI
  http://act-r.psy.cmu.edu/~douglass/Douglass/Agents/TopicPapers/~TopCandidates~/Robotics/brooks87intelligence.pdf
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnuyuq7xF&sig=605f19sD3y7t4DoispNMxvmyy3Y
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnvuuu4CF&sig=DAI5FtzBm8byWUcnf5U3qBR0ihk
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnryrt5CC&sig=HHybOULxfTdFwfwaliV_tWAF-rY
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncntqss8yH&sig=qiqJVUCetwl0cyAg5v_FQkGQePw
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncntwsx7vB&sig=CcPsaAISyDSP5mOKB52AxYOWprI
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnqwqt7AE&sig=kaOJuTY3bDJqBoxZyS0StZghsM4
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=NbwtvuyawC&sig=ZOLRXpLrB5DFvnDlSf6wNaw3gYg
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Nbwtqrsaty&sig=wE7wq6m3Inq7Z4qwmXLucc7RI00
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Nbwttux6wz&sig=LYLowQNnoYxvhtwzFNt0X9gEQ4Y
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnustw1wE&sig=eFSDUHWbsnHEh9S4Zn_paKguMWc
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnpupt8BB&sig=lN7SP76JP7P7VOlJEKQ4NEl3MkU
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=NcnqqpyaBB&sig=WBYA3src9nEuAgdYTkk4CjPHrqQ
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA139&ots=Ncnsury1zy&sig=gzFoWrIH5zSH1JxwjugtFKvBPxs
  
• Andy Clark & Josefa Toribio (1994). Doing without representing.   (Abstract & more)
  Abstract:   Connectionism and classicism, it generally appears, have at least this much in common: both place some notion of internal representation at the heart of a scientific study of mind. In recent years, however, a much more radical view has gained increasing popularity. This view calls into question the commitment to internal representation itself. More strikingly still, this new wave of anti-representationalism is rooted not in armchair theorizing but in practical attempts to model and understand intelligent, adaptive behavior. In this paper we first present, and then critically assess, a variety of recent anti-representationalist treatments. We suggest that so far, at least, the sceptical rhetoric outpaces both evidence and argument. Some probable causes of this premature scepticism are isolated. Nonetheless, the anti-representationalist challenge is shown to be both important and progressive insofar as it forces us to see beyond the bare representational/non-representational dichotomy and to recognize instead a rich continuum of degrees and types of representationality
  Additional links for this entry:
  http://wexler.free.fr/library/files/clark (1994) doing without representing.pdf
  http://www.springerlink.com/content/w02t5nm8j7164v10/fulltext.pdf
  http://www.springerlink.com/index/W02T5NM8J7164V10.pdf
  
• Daniel C. Dennett (1989). Cognitive ethology.   (Abstract & more)
  Abstract: The field of Artificial Intelligence has produced so many new concepts--or at least vivid and more structured versions of old concepts--that it would be surprising if none of them turned out to be of value to students of animal behavior. Which will be most valuable? I will resist the temptation to engage in either prophecy or salesmanship; instead of attempting to answer the question: "How might Artificial Intelligence inform the study of animal behavior?" I will concentrate on the obverse: "How might the study of animal behavior inform research in Artificial Intelligence?"
• Ruth G. Millikan (online). On reading signs.   (Abstract & more)
  Abstract: On Reading Signs; Some Differences between Us and The Others If there are certain kinds of signs that an animal cannot learn to interpret, that might be for any of a number of reasons. It might be, first, because the animal cannot discriminate the signs from one another. For example, although human babies learn to discriminate human speech sounds according to the phonological structures of their native languages very easily, it may be that few if any other animals are capable of fully grasping the phonological structures of human languages. If an animal cannot learn to interpret certain signs it might be, second, because the decoding is too difficult for it. It could be, for example, that some animals are incapable of decoding signs that exhibit syntactic embedding, or signs that are spread out over time as opposed to over space. Problems of these various kinds might be solved by using another sign system, say, gestures rather than noises, or visual icons laid out in spatial order, or by separating out embedded propositions and presenting each separately. But a more interesting reason that an animal might be incapable of understanding a sign would be that it lacked mental representations of the necessary kind. It might be incapable of representing mentally what the sign conveys. When discussing what signs animals can understand or
  Additional links for this entry:
  http://www.ucc.uconn.edu/~wwwphil/lorenz.pdf
  http://www.philosophy.uconn.edu/department/millikan/lorenz.pdf
  http://www.philosophy.uconn.edu/department/millikan/lorenz.pdf
  http://books.google.com/books?hl=en&lr=&id=GsSWKPB6KW8C&oi=fnd&pg=PA15&dq=On+reading+signs+Millikan&ots=l7qcSFGra0&sig=uhILZgZBxuWhg03CdjzvarNZ8wc
  
• David Kirsh (1991). Today the earwig, tomorrow man?   (Abstract & more)
  Abstract: A startling amount of intelligent activity can be controlled without reasoning or thought. By tuning the perceptual system to task relevant properties a creature can cope with relatively sophisticated environments without concepts. There is a limit, however, to how far a creature without concepts can go. Rod Brooks, like many ecologically oriented scientists, argues that the vast majority of intelligent behaviour is concept-free. To evaluate this position I consider what special benefits accrue to concept-using creatures. Concepts are either necessary for certain types of perception, learning, and control, or they make those processes computationally simpler. Once a creature has concepts its capacities are vastly multiplied.
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2506371CI
  
• Vincent C. Müller (2007). Is there a future for AI without representation?   (Abstract & more)
  Abstract: This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks’ proposal for cognition without representation appears promising for full-blown intelligent agents—Though not for conscious agents
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=1285561.1285568
  http://www.springerlink.com/index/T65JK1H2705383L8.pdf
  http://www.typos.de/pdf/2007_AI_without_representation_M&M.pdf
  http://www.springerlink.com/content/t65jk1h2705383l8/fulltext.pdf
  
• Tim van Gelder (1995). What might cognition be if not computation?   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=FEnPpGB6HE0C&oi=fnd&pg=PT158&ots=iLWO_4GjfB&sig=_-hLgEKPrvzRGqiHU3WB-ByPxG4
  http://books.google.com/books?hl=en&lr=&id=FEnPpGB6HE0C&oi=fnd&pg=PT158&ots=iK3S02IheD&sig=9bvnduaEm9LGyoZvLb9vrkbzLwc
  http://books.google.com/books?hl=en&lr=&id=FEnPpGB6HE0C&oi=fnd&pg=PT158&ots=iLWOW7Jo9y&sig=nbynhfg_nmoW_oos5qyFfZKaq_w
  http://www.jstor.org/sici?sici=0022-362X(199507)92:7<345:WMCBIN>2.0.CO;2-F
  http://www.jstor.org/stable/pdfplus/2941061.pdf
  
• Peter Wallis (2004). Intention without representation.   (Abstract & more)
  Abstract: A mechanism for planning ahead would appear to be essential to any creature with more than insect level intelligence. In this paper it is shown how planning, using full means-ends analysis, can be had while avoiding the so called symbol grounding problem. The key role of knowledge representation in intelligence has been acknowledged since at least the enlightenment, but the advent of the computer has made it possible to explore the limits of alternate schemes, and to explore the nature of our everyday understanding of the world around us. In particular, artificial intelligence (AI) and robotics has forced a close examination, by people other than philosophers, of what it means to say for instance that "snow is white." One interpretation of the "new AI" is that it is questioning the need for representation altogether. Brooks and others have shown how a range of intelligent behaviors can be had without representation, and this paper goes one step further showing how intending to do things can be achieved without symbolic representation. The paper gives a concrete example of a mechanism in terms of robots that play soccer. It describes a belief, desire and intention (BDI) architecture that plans in terms of activities. The result is a situated agent that plans to do things with no more ontological commitment than the reactive systems Brooks described in his seminal paper, "Intelligence without Representation."
  Additional links for this entry:
  http://www.informaworld.com/smpp/./ftinterface~content=a713630032~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/T86XG5YMR7KE123J.pdf
  http://www.informaworld.com/index/713630032.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2004/00000017/00000002/art00004
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a713630032~fulltext=713240930
  
• Jonathan Webber (2002). Doing without representation: Coping with Dreyfus.   (Abstract & more)
  Abstract: Hubert Dreyfus argues that the traditional and currently dominant conception of an action, as an event initiated or governed by a mental representation of a possible state of affairs that the agent is trying to realise, is inadequate. If Dreyfus is right, then we need a new conception of action. I argue, however, that the considerations that Dreyfus adduces show only that an action need not be initiated or governed by a conceptual representation, but since a representation need not be conceptually structured, do not show that we need a conception of action that does not involve representation
  Additional links for this entry:
  http://www.bristol.ac.uk/philosophy/department/staff/JW/DoingWithoutRepresentation.pdf
  http://www.informaworld.com/smpp/./ftinterface~content=a782351921~fulltext=713240930
  http://www.informaworld.com/index/782351921.pdf
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a782351921~fulltext=713240930
  

Computation and Representation, Misc

• Varol Akman & Paul J. W. ten Hagen (1989). The power of physical representations.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=65965.69627
  http://www.cs.bilkent.edu.tr/~akman/jour-papers/aimag/aimag1989-2.pdf
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2227604CI
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=N8920612AH
  
• David J. Chalmers, Robert M. French & Douglas R. Hofstadter (1992). High-level perception, representation, and analogy:A critique of artificial intelligence methodology.   (Abstract & more)
  Abstract: High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context
  Additional links for this entry:
  http://citeseer.ist.psu.edu/49715.html
  http://portal.acm.org/citation.cfm?id=175346.175347
  http://www.u.arizona.edu/~chalmers/papers/highlevel.pdf
  http://www.nbu.bg/cogs/personal/kokinov/COG501/hightlevel.pdf
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2767459CI
  http://www.informaworld.com/index/778787585.pdf
  
• Terry Dartnall (2000). Reverse psychologism, cognition and content.   (Abstract & more)
  Abstract:   The confusion between cognitive states and the content of cognitive states that gives rise to psychologism also gives rise to reverse psychologism. Weak reverse psychologism says that we can study cognitive states by studying content – for instance, that we can study the mind by studying linguistics or logic. This attitude is endemic in cognitive science and linguistic theory. Strong reverse psychologism says that we can generate cognitive states by giving computers representations that express the content of cognitive states and that play a role in causing appropriate behaviour. This gives us strong representational, classical AI (REPSCAI), and I argue that it cannot succeed. This is not, as Searle claims in his Chinese Room Argument, because syntactic manipulation cannot generate content. Syntactic manipulation can generate content, and this is abundantly clear in the Chinese Room scenano. REPSCAI cannot succeed because inner content is not sufficient for cognition, even when the representations that carry the content play a role in generating appropriate behaviour
  Additional links for this entry:
  http://www98.griffith.edu.au/dspace/handle/10072/3245
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=475243CI
  http://www.springerlink.com/content/content/r64783586474k030/fulltext.pdf
  http://www.springerlink.com/content/r64783586474k030/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=231754&PDF=1
  http://www.springerlink.com/index/R64783586474K030.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000001/00231754
  
• James Franklin, The representation of context: Ideas from artificial intelligence.   (Abstract & more)
  Abstract: To move beyond vague platitudes about the importance of context in legal reasoning or natural language understanding, one must take account of ideas from artificial intelligence on how to represent context formally. Work on topics like prior probabilities, the theory-ladenness of observation, encyclopedic knowledge for disambiguation in language translation and pathology test diagnosis has produced a body of knowledge on how to represent context in artificial intelligence applications
• Francisco Calvo Garzon & Angel Garcia Rodriguez (2009). Where is cognitive science heading?   (Abstract & more)
  Abstract: According to Ramsey (Representation reconsidered, Cambridge University Press, New York, 2007), only classical cognitive science, with the related notions of input–output and structural representations, meets the job description challenge (the challenge to show that a certain structure or process serves a representational role at the subpersonal level). By contrast, connectionism and other nonclassical models, insofar as they exploit receptor and tacit notions of representation, are not genuinely representational. As a result, Ramsey submits, cognitive science is taking a U-turn from representationalism back to behaviourism, thus presupposing that (1) the emergence of cognitivism capitalized on the concept of representation, and that (2) the materialization of nonclassical cognitive science involves a return to some form of pre-cognitivist behaviourism. We argue against both (1) and (2), by questioning Ramsey’s divide between classical and representational, versus nonclassical and nonrepresentational, cognitive models. For, firstly, connectionist and other nonclassical accounts have the resources to exploit the notion of a structural isomorphism, like classical accounts (the beefing-up strategy); and, secondly, insofar as input–output and structural representations refer to a cognitive agent, classical explanations fail to meet the job description challenge (the deflationary strategy). Both strategies work independently of each other: if the deflationary strategy succeeds, contra (1), cognitivism has failed to capitalize on the relevant concept of representation; if the beefing-up strategy is sound, contra (2), the return to a pre-cognitivist era cancels out.
• John Haugeland (1981). Semantic engines: An introduction to mind design.   (More)
• Leslie Marsh (2005). Review Essay: Andy Clark's Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence_.   (Abstract & more)
  Abstract: The notion of the cyborg has exercised the popular imagination for almost two hundred years. In very general terms the idea that a living entity can be a hybrid of both organic matter and mechanical parts, and for all intents and purposes be seamlessly functional and self-regulating, was prefigured in literary works such as Shelly
  s Frankenstein (1816/18) and Samuel Butler
  s Erewhon (1872). This notion of hybridism has been a staple theme of 20th century science fiction writing, television programmes and the cinema. For the most part, these works trade on a deep sense of unease we have about our personal identity – how could some non-organic matter to which I have so little conscious access count as a bona fide part of me? Cognitive scientist and philosopher, Andy Clark, picks up this general theme and presents an empirical and philosophical case for the following inextricably linked theses.
• Itay Shani (2005). Computation and intentionality: A recipe for epistemic impasse.   (Abstract & more)
  Abstract: Searle’s celebrated Chinese room thought experiment was devised as an attempted refutation of the view that appropriately programmed digital computers literally are the possessors of genuine mental states. A standard reply to Searle, known as the “robot reply” (which, I argue, reflects the dominant approach to the problem of content in contemporary philosophy of mind), consists of the claim that the problem he raises can be solved by supplementing the computational device with some “appropriate” environmental hookups. I argue that not only does Searle himself casts doubt on the adequacy of this idea by applying to it a slightly revised version of his original argument, but that the weakness of this encoding-based approach to the problem of intentionality can also be exposed from a somewhat different angle. Capitalizing on the work of several authors and, in particular, on that of psychologist Mark Bickhard, I argue that the existence of symbol-world correspondence is not a property that the cognitive system itself can appreciate, from its own perspective, by interacting with the symbol and therefore, not a property that can constitute intrinsic content. The foundational crisis to which Searle alluded is, I conclude, very much alive
  Additional links for this entry:
  http://www.springerlink.com/content/k26t830883g84041/fulltext.pdf
  http://www.springerlink.com/index/K26T830883G84041.pdf
  http://www.ingentaconnect.com/content/klu/mind/2005/00000015/00000002/00002004
  
• Jason Stanley (2005). Review of Robyn Carston, Thoughts and Utterances.   (Abstract & more)
  Abstract: Relevance Theory is the influential theory of linguistic interpretation first championed by Dan Sperber and Deirdre Wilson. Relevance theorists have made important contributions to our understanding of a wide range of constructions, especially constructions that tend to receive less attention in semantics and philosophy of language. But advocates of Relevance Theory also have had a tendency to form a rather closed community, with an unwillingness to translate their own special vocabulary and distinctions into more neutral vernacular. Since Robyn Carston has long been the advocate of Relevance Theory most able to communicate with a broader philosophical and linguistic audience, it is with particular interest that the emergence of her long-awaited volume, Thoughts and Utterances has been greeted. The volume exhibits many of the strengths, but also some of the weaknesses, of this well-known program
• Chris Thornton (1997). Brave mobots use representation: Emergence of representation in fight-or-flight learning.   (Abstract & more)
  Abstract:   The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provide a model of the development of a fight-or-flight response in a simulated agent. The modelled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structures which can be given a representational interpretation. It also shows how these may form the infrastructure for closely-coupled agent/environment interaction
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=596763
  http://www.cogs.susx.ac.uk/users/christ/fight-or-flight.html
  http://www.cogs.susx.ac.uk/users/christ/papers/brave-mobots.ps
  http://www.cogs.sussex.ac.uk/users/christ/papers/brave-mobots.ps
  http://www.informatics.sussex.ac.uk/users/christ/papers/brave-mobots.ps
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=316236CI
  http://www.springerlink.com/content/q228732u4h164l35/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=136246&PDF=1
  http://www.springerlink.com/index/Q228732U4H164L35.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000004/00136246
  

Philosophy of Connectionism

Connectionism and Compositionality

• Kenneth Aizawa (1997). The role of the systematicity argument in classicism and connectionism.   (More)
• Murat Aydede (1997). Language of thought: The connectionist contribution.   (Abstract & more)
  Abstract:   Fodor and Pylyshyn's critique of connectionism has posed a challenge to connectionists: Adequately explain such nomological regularities as systematicity and productivity without postulating a "language of thought" (LOT). Some connectionists like Smolensky took the challenge very seriously, and attempted to meet it by developing models that were supposed to be non-classical. At the core of these attempts lies the claim that connectionist models can provide a representational system with a combinatorial syntax and processes sensitive to syntactic structure. They are not implementation models because, it is claimed, the way they obtain syntax and structure sensitivity is not "concatenative," hence "radically different" from the way classicists handle them. In this paper, I offer an analysis of what it is to physically satisfy/realize a formal system. In this context, I examine the minimal truth-conditions of LOT Hypothesis. From my analysis it will follow that concatenative realization of formal systems is irrelevant to LOTH since the very notion of LOT is indifferent to such an implementation level issue as concatenation. I will conclude that to the extent to which they can explain the law-like cognitive regularities, a certain class of connectionist models proposed as radical alternatives to the classical LOT paradigm will in fact turn out to be LOT models, even though new and potentially very exciting ones
  Additional links for this entry:
  http://www.springerlink.com/content/content/v8365g5vp26x7x75/fulltext.pdf
  http://www.springerlink.com/content/v8365g5vp26x7x75/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=115552&PDF=1
  http://www.springerlink.com/index/V8365G5VP26X7X75.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000001/00115552
  
• David J. Chalmers (1993). Connectionism and compositionality: Why Fodor and Pylyshyn were wrong.   (Abstract & more)
  Abstract: This paper offers both a theoretical and an experimental perspective on the relationship between connectionist and Classical (symbol-processing) models. Firstly, a serious flaw in Fodor and Pylyshyn’s argument against connectionism is pointed out: if, in fact, a part of their argument is valid, then it establishes a conclusion quite different from that which they intend, a conclusion which is demonstrably false. The source of this flaw is traced to an underestimation of the differences between localist and distributed representation. It has been claimed that distributed representations cannot support systematic operations, or that if they can, then they will be mere implementations of traditional ideas. This paper presents experimental evidence against this conclusion: distributed representations can be used to support direct structure-sensitive operations, in a man- ner quite unlike the Classical approach. Finally, it is argued that even if Fodor and Pylyshyn’s argument that connectionist models of compositionality must be mere implementations were correct, then this would still not be a serious argument against connectionism as a theory of mind
• David J. Chalmers (online). Deep systematicity and connectionist representation.   (Abstract & more)
  Abstract: 1. I think that by emphasizing theoretical spaces of representations, Andy has put his finger on an issue that is key to connectionism's success, and whose investigation will be a key determinant of the field's further progress. I also think that if we look at representational spaces in the right way, we can see that they are deeply related to classical phenomenon of systematicity in representation. I want to argue that the key to understanding representational spaces, and in particular their ability to capture the deep organization underlying various problems, lies in the idea of what I will call
  Additional links for this entry:
  http://consc.net/notes/clark-comments.html
  
• David J. Chalmers (1990). Syntactic transformations on distributed representations.   (Abstract & more)
  Abstract: There has been much interest in the possibility of connectionist models whose representations can be endowed with compositional structure, and a variety of such models have been proposed. These models typically use distributed representations that arise from the functional composition of constituent parts. Functional composition and decomposition alone, however, yield only an implementation of classical symbolic theories. This paper explores the possibility of moving beyond implementation by exploiting holistic structure-sensitive operations on distributed representations. An experiment is performed using Pollack’s Recursive Auto-Associative Memory. RAAM is used to construct distributed representations of syntactically structured sentences. A feed-forward network is then trained to operate directly on these representations, modeling syn- tactic transformations of the represented sentences. Successful training and generalization is obtained, demonstrating that the implicit structure present in these representations can be used for a kind of structure-sensitive processing unique to the connectionist domain
  Additional links for this entry:
  http://citeseer.ist.psu.edu/517265.html
  http://citeseer.ist.psu.edu/chalmers90syntactic.html
  http://www.u.arizona.edu/~chalmers/papers/transformations.pdf
  http://www.informaworld.com/index/776705615.pdf
  
• M. H. Christiansen & Nick Chater (1994). Generalization and connectionist language learning.   (More)
  Additional links for this entry:
  http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0017.1994.tb00226.x
  http://www3.interscience.wiley.com/cgi-bin/fulltext/119275792/PDFSTART
  
• Jerry A. Fodor & Zenon W. Pylyshyn (1988). Connectionism and cognitive architecture.   (Abstract & more)
  Abstract: This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=58067
  http://www.blutner.de/philom/connect/jaf.pdf
  http://ruccs.rutgers.edu/ftp/pub/papers/jaf.pdf
  http://portal.acm.org/citation.cfm?id=190704.190740
  http://www.ii.metu.edu.tr/~tekman/fodor&pylyshyn.pdf
  http://citeseer.ist.psu.edu/fodor88connectionism.html
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bLzaEHRW&sig=1PDJYThCtCF24M7R6UJK4T
  http://watarts.uwaterloo.ca/~celiasmi/courses/Phil256/papers/fodor.pylyshyn.1988.Connectionism cog architec.bbs.pdf
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bKr8GASZ&sig=lvbjst___yAcJlOgVzyRdzEV09k
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  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKwbCDTZ&sig=00wE8Svuh3W8kqsUo0UKOYCfcAI
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  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bKx9AJPT&sig=7sjHbKdagqCd6sUT-Kk-fcITZlc
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bHx6HEPT&sig=9ypKOA18BrlaitMu_it-TVCBfUw
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKr7GFP0&sig=tdUqyz4ARusp_kokrKNOJedNknk
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  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bJv8BJW0&sig=Qe8qR4Go6tnV95LlvTT6ELC_0Ds
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKwbCBVX&sig=iCTIyYq8EOvBdYu-srEXgyt-HoM
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O2dHfvmUIJ&sig=7hnYM8j4zdtpN4THjVSpzpGFEDs
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34InykTQC&sig=F0CONzdT49KnmoIp41iOngjBa_A
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34IhygWIA&sig=Zks6FRmma9XZGHW2EO1kvwIxesQ
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34DjtnWOI&sig=ohgYrPGd2Ut0_gQqvfJPXg0_xJo
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34FnvnRJB&sig=snID_Ptc5j-K-DxZvSjAScHkD6s
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34GjwhYPJ&sig=YnoEHTZY6rwzlL8DcfCj45uUQo0
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34JjynVOD&sig=XBIWzRchQULcgPTlX6HyBgOg_pQ
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34HfwmPLG&sig=35GDmDcPG526574Ar-pZaGlfYXA
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34ElunTIA&sig=OzlrEEZ2Td9CGt5mgUD99UXGCWE
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34HlxgYIA&sig=ZsqjFV8LrCGXI-3jf8Pliu_sVsc
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34FhvhWIA&sig=1yq1WHwwotr3qVSaqN0K4StIWc8
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34EfuiUKI&sig=lDwkN-dMTxlLX_1XELpsD4ONtM4
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O2dHizhQJF&sig=s7d-fmzg598bYH-IpAMNrcyO8eo
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O2dHkziQOE&sig=6AJnryXo_R8vj8qseWkK5DD31zQ
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=2450716&dopt=Citation
  
• Robert F. Hadley (1997). Cognition, systematicity, and nomic necessity.   (More)
  Additional links for this entry:
  http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0017.1997.tb00066.x
  http://www3.interscience.wiley.com/cgi-bin/fulltext/119154897/PDFSTART
  http://www.blackwell-synergy.com/links/doi/10.1111/1468-0017.00040
  http://www.blackwell-synergy.com/doi/abs/10.1111/1468-0017.00040
  http://www.ingentaconnect.com/content/bpl/mila/1997/00000012/00000002/art00002
  http://www.ingentaconnect.com/content/bpl/mila/1997/00000012/00000002/art00040
  
• Robert F. Hadley (1997). Explaining systematicity: A reply to Kenneth Aizawa.   (Abstract & more)
  Abstract:   In his discussion of results which I (with Michael Hayward) recently reported in this journal, Kenneth Aizawa takes issue with two of our conclusions, which are: (a) that our connectionist model provides a basis for explaining systematicity within the realm of sentence comprehension, and subject to a limited range of syntax (b) that the model does not employ structure-sensitive processing, and that this is clearly true in the early stages of the network''s training. Ultimately, Aizawa rejects both (a) and (b) for reasons which I think are ill-founded. In what follows, I offer a defense of our position. In particular, I argue (1) that Aizawa adopts a standard of explanation that many accepted scientific explanations could not meet, and (2) that Aizawa misconstrues the relevant meaning of structure-sensitive process
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=316242CI
  http://www.springerlink.com/content/m395128140134l86/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=122810&PDF=1
  http://www.springerlink.com/index/M395128140134L86.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000004/00122810
  
• Robert F. Hadley (1994). Systematicity in connectionist language learning.   (More)
  Additional links for this entry:
  http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0017.1994.tb00225.x
  http://www3.interscience.wiley.com/cgi-bin/fulltext/119275791/PDFSTART
  
• Robert F. Hadley & M. B. Hayward (1997). Strong semantic systematicity from Hebbian connectionist learning.   (Abstract & more)
  Abstract:   Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words in syntactic positions they did not occupy during training. The experience of researchers indicates that strong systematicity in any form is difficult to achieve in connectionist systems.Herein we describe a network which displays strong semantic systematicity in response to Hebbian, connectionist training. During training, two-thirds of all nouns are presented only in a single syntactic position (either as grammatical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levels of embedding. Successful training requires a, corpus of about 1000 sentences, and network training is quite rapid. The architecture and learning algorithms are purely connectionist, but classical insights are discernible in one respect, viz, that complex semantic representations spatially contain their semantic constituents. However, in other important respects, the architecture is distinctly non-classical
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=596704.596727
  http://www.springerlink.com/content/k4l352163l5777l2/fulltext.pdf
  http://www.springerlink.com/index/K4L352163L5777L2.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000001/00112501
  
• W. F. G. Haselager & J. F. H. Van Rappard (1998). Connectionism, systematicity, and the frame problem.   (Abstract & more)
  Abstract:   This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts can be raised about the potential of distributed representations to allow large amounts of complexly structured information to be adequately encoded and processed. It is questionable whether connectionist models that are claimed to effectively represent structured information can be scaled up to a realistic extent. We conclude that the frame problem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=371129CI
  http://www.springerlink.com/content/um0x474450532866/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=150466&PDF=1
  http://www.springerlink.com/index/UM0X474450532866.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000008/00000002/00150466
  
• L. F. Niklasson & Tim van Gelder (1994). On being systematically connectionist.   (Abstract & more)
  Abstract: In 1988 Fodor and Pylyshyn issued a challenge to the newly-popular connectionism: explain the systematicity of cognition without merely implementing a so-called classical architecture. Since that time quite a number of connectionist models have been put forward, either by their designers or by others, as in some measure demonstrating that the challenge can be met (e.g., Pollack, 1988, 1990; Smolensky, 1990; Chalmers, 1990; Niklasson and Sharkey, 1992; Brousse, 1993). Unfortu- nately, it has generally been unclear whether these models actually do have this implication (see, for instance, the extensive philosophical debate in Smolensky, 1988; Fodor and McLaughlin, 1990; van Gelder, 1990, 1991; McLaughlin, 1993a, 1993b; Clark, 1993). Indeed, we know of no major supporter of classical orthodoxy who has felt compelled, by connectionist models and argu- ments, to concede in print that connectionists have in fact delivered a non-classical explanation of systematicity
  Additional links for this entry:
  http://citeseer.ist.psu.edu/niklasson94being.html
  http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-0017.1994.tb00227.x
  
• Stephen H. Phillips (2002). Does classicism explain universality?   (Abstract & more)
  Abstract:   One of the hallmarks of human cognition is the capacity to generalize over arbitrary constituents. Recently, Marcus (1998, 1998a, b; Cognition 66, p. 153; Cognitive Psychology 37, p. 243) argued that this capacity, called universal generalization (universality), is not supported by Connectionist models. Instead, universality is best explained by Classical symbol systems, with Connectionism as its implementation. Here it is argued that universality is also a problem for Classicism in that the syntax-sensitive rules that are supposed to provide causal explanations of mental processes are either too strict, precluding possible generalizations; or too lax, providing no information as to the appropriate alternative. Consequently, universality is not explained by a Classical theory
  Additional links for this entry:
  http://www.springerlink.com/content/9lycmrkn91j71qqe/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=408595&PDF=1
  http://www.springerlink.com/index/9LYCMRKN91J71QQE.pdf
  
• Jordan B. Pollack (1990). Recursive distributed representations.   (More)
  Additional links for this entry:
  http://demo.cs.brandeis.edu/papers/raam.pdf
  http://portal.acm.org/citation.cfm?id=102423
  http://www.ing.unisi.it/~monica/Paper/p19.ps.gz
  http://www.uow.edu.au/~markus/apods/doc/papers/raam.ps
  http://www.blutner.de/philom/connect/pollack90recursive.pdf
  http://www.blutner.de/NeuralNets/Texts/pollack90recursive.pdf
  http://www.idi.ntnu.no/emner/it3708/archive/2004/files/raam.pdf
  http://ftp.uni-koeln.de/neural-nets/papers/pollack.newraam.ps.gz
  http://www.cse.ohio-state.edu/~jj/pub/papers/89-JP-RECURSIVE.ps.Z
  http://www.umiacs.umd.edu/~ymarton/ling849/pollack90recursive.pdf
  http://ftp.funet.fi/pub/sci/neural/neuroprose/pollack.newraam.ps.Z
  http://www.funet.fi/pub/sci/neural/neuroprose/pollack.newraam.ps.Z
  http://www.ftp.funet.fi/ftp/pub/sci/ai/neural/neuroprose/pollack.newraam.ps.Z
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2470980CI
  
• Paul Smolensky (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems.   (More)
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2470919CI
  
• Tim van Gelder (1990). Compositionality: A connectionist variation on a classical theme.   (More)
  Additional links for this entry:
  http://www.leaonline.com/doi/pdfplus/10.1207/s15516709cog1403_2
  http://www.cogsci.rpi.edu/CSJarchive/1990v14/i03/p0355p0384/MAIN.PDF
  
• Tim van Gelder (online). Can connectionist models exhibit non-classical structure sensitivity?   (Abstract & more)
  Abstract: Department of Computer Science Philosophy Program, Research School of Social Sciences University of Skövde, S-54128, SWEDEN Australian National University, Canberra ACT 0200
  Additional links for this entry:
  http://citeseer.ist.psu.edu/niklasson94can.html
  
• Tim van Gelder (1994). On being systematically connectionist.   (Abstract & more)
  Abstract: In 1988 Fodor and Pylyshyn issued a challenge to the newly-popular connectionism: explain the systematicity of cognition without merely implementing a so-called classical architecture. Since that time quite a number of connectionist models have been put forward, either by their designers or by others, as in some measure demonstrating that the challenge can be met (e.g., Pollack, 1988, 1990; Smolensky, 1990; Chalmers, 1990; Niklasson and Sharkey, 1992; Brousse, 1993). Unfortu- nately, it has generally been unclear whether these models actually do have this implication (see, for instance, the extensive philosophical debate in Smolensky, 1988; Fodor and McLaughlin, 1990; van Gelder, 1990, 1991; McLaughlin, 1993a, 1993b; Clark, 1993). Indeed, we know of no major supporter of classical orthodoxy who has felt compelled, by connectionist models and argu- ments, to concede in print that connectionists have in fact delivered a non-classical explanation of systematicity
  Additional links for this entry:
  http://citeseer.ist.psu.edu/niklasson94being.html
  
• Jonathan A. Waskan & William P. Bechtel (1997). Directions in connectionist research: Tractable computations without syntactically structured representations.   (Abstract & more)
  Abstract: Figure 1: A pr ototyp ical exa mple of a three-layer feed forward network, used by Plunkett and M archm an (1 991 ) to simulate learning the past-tense of En glish verbs. The inpu t units encode representations of the three phonemes of the present tense of the artificial words used in this simulation. Th e netwo rk is trained to produce a representation of the phonemes employed in the past tense form and the suffix (/d/, /ed/, or /t/) used on regular verbs. To run the network, each input unit is assigned an activation value o f 0 or 1 , dep ending on whethe r the featu re is present or not. Eac h input unit is conne cted to each of the 30 hidden units by a we ighted conn ection and p rovid es an inp ut to each hidden unit equal to the product of the input unit's activation and the weight. Each hidd en unit's activation is then determined by summing ov er the va lues co ming fro m each inp ut unit to deter mine a netinput, and then applying a non-linear function (e.g., the logistic function 1/(1+e^netinput). Th is whole proced ure is
  Additional links for this entry:
  http://www.blackwell-synergy.com/links/doi/10.1111/1467-9973.00040
  http://www.blackwell-synergy.com/doi/abs/10.1111/1467-9973.00040
  http://www.ingentaconnect.com/content/bpl/meta/1997/00000028/F0020001/art00002
  http://www.ingentaconnect.com/content/bpl/meta/1997/00000028/f0020001
  

Representation in Connectionism

• William P. Bechtel (1994). Natural deduction in connectionist systems.   (Abstract & more)
  Abstract:   The relation between logic and thought has long been controversial, but has recently influenced theorizing about the nature of mental processes in cognitive science. One prominent tradition argues that to explain the systematicity of thought we must posit syntactically structured representations inside the cognitive system which can be operated upon by structure sensitive rules similar to those employed in systems of natural deduction. I have argued elsewhere that the systematicity of human thought might better be explained as resulting from the fact that we have learned natural languages which are themselves syntactically structured. According to this view, symbols of natural language are external to the cognitive processing system and what the cognitive system must learn to do is produce and comprehend such symbols. In this paper I pursue that idea by arguing that ability in natural deduction itself may rely on pattern recognition abilities that enable us to operate on external symbols rather than encodings of rules that might be applied to internal representations. To support this suggestion, I present a series of experiments with connectionist networks that have been trained to construct simple natural deductions in sentential logic. These networks not only succeed in reconstructing the derivations on which they have been trained, but in constructing new derivations that are only similar to the ones on which they have been trained
  Additional links for this entry:
  http://citeseer.ist.psu.edu/bechtel94natural.html
  http://www.blutner.de/NeuralNets/Texts/DERIVATI.pdf
  http://mechanism.ucsd.edu/~bill/research/DERIVATI.pdf
  http://www.springerlink.com/content/q71158t2342jl517/fulltext.pdf
  http://www.springerlink.com/index/Q71158T2342JL517.pdf
  
• Andy Clark (ms). Connectionism, nonconceptual content, and representational redescription.   (More)
• Robert E. Cummins (1991). The role of representation in connectionist explanation of cognitive capacities.   (More)
• Adrian Cussins (1990). The connectionist construction of concepts.   (Abstract & more)
  Abstract: The character of computational modelling of cognition depends on an underlying theory of representation. Classical cognitive science has exploited the syntax/semantics theory of representation that derives from logic. But this has had the consequence that the kind of psychological explanation supported by classical cognitive science is

  _conceptualist_:

  psychological phenomena are modelled in terms of relations that hold between concepts, and between the sensors/effectors and concepts. This kind of explanation is inappropriate for the Proper Treatment of Connectionism (Smolensky 1988)
• Chris Eliasmith (online). Structure without symbols: Providing a distributed account of high-level cognition.   (Abstract & more)
  Abstract: There has been a long-standing debate between symbolicists and connectionists concerning the nature of representation used by human cognizers. In general, symbolicist commitments have allowed them to provide superior models of high-level cognitive function. In contrast, connectionist distributed representations are preferred for providing a description of low-level cognition. The development of Holographic Reduced Representations (HRRs) has opened the possibility of one representational medium unifying both low-level and high-level descriptions of cognition. This paper describes the relative strengths and weaknesses of symbolic and distributed representations. HRRs are shown to capture the important strengths of both types of representation. These properties of HRRs allow a rebuttal of Fodor and McLaughlin's (1990) criticism that distributed representations are not adequately structure sensitive to provide a full account of human cognition
• Robert F. Hadley (2004). On the proper treatment of semantic systematicity.   (Abstract & more)
  Abstract:   The past decade has witnessed the emergence of a novel stance on semantic representation, and its relationship to context sensitivity. Connectionist-minded philosophers, including Clark and van Gelder, have espoused the merits of viewing hidden-layer, context-sensitive representations as possessing semantic content, where this content is partially revealed via the representations'' position in vector space. In recent work, Bodén and Niklasson have incorporated a variant of this view of semantics within their conception of semantic systematicity. Moreover, Bodén and Niklasson contend that they have produced experimental results which not only satisfy a kind of context-based, semantic systematicity, but which, to the degree that reality permits, effectively deals with challenges posed by Fodor and Pylyshyn (1988), and Hadley (1994a). The latter challenge involved well-defined criteria for strong semantic systematicity. This paper examines the relevant claims and experiments of Bodén and Niklasson. It is argued that their case fatally involves two fallacies of equivocation; one concerning ''semantic content'' and the other concerning ''novel test sentences''. In addition, it is argued that their ultimate construal of context sensitive semantics contains serious confusions. These confusions are also found in certain publications dealing with "latent semantic analysis". Thus, criticisms presented here have relevance beyond the work of Bodén and Niklasson
  Additional links for this entry:
  http://www.springerlink.com/content/p4312417q27100p0/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5142070&PDF=1
  http://www.springerlink.com/index/P4312417Q27100P0.pdf
  http://www.ingentaconnect.com/content/klu/mind/2004/00000014/00000002/05142070
  
• W. F. G. Haselager (1999). On the potential of non-classical constituency.   (More)
  Additional links for this entry:
  http://www.nici.kun.nl/~haselag/publications/1999c.pdf
  http://www.socsci.kun.nl/~haselag/publications/1999c.pdf
  http://www.nici.ru.nl/~haselag/publications/NonClassConst99.pdf
  http://www.nici.kun.nl/~haselag/publications/NonClassConst99.pdf
  http://www.socsci.kun.nl/~haselag/publications/NonClassConst99.pdf
  
• Daniel M. Haybron (2000). The causal and explanatory role of information stored in connectionist networks.   (Abstract & more)
  Abstract:   In this paper I defend the propriety of explaining the behavior of distributed connectionist networks by appeal to selected data stored therein. In particular, I argue that if there is a problem with such explanations, it is a consequence of the fact that information storage in networks is superpositional, and not because it is distributed. I then develop a ``proto-account'''' of causation for networks, based on an account of Andy Clark''s, that shows even superpositionality does not undermine information-based explanation. Finally, I argue that the resulting explanations are genuinely informative and not vacuous
  Additional links for this entry:
  http://www.slu.edu/users/users/haybrond/Connectionist information storage.pdf
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=536479CI
  http://www.springerlink.com/content/content/t061056966k44051/fulltext.pdf
  http://www.springerlink.com/content/t061056966k44051/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=258827&PDF=1
  http://www.springerlink.com/index/T061056966K44051.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000003/00258827
  
• Aarre Laakso & Garrison W. Cottrell (2000). Content and cluster analysis: Assessing representational similarity in neural systems.   (Abstract & more)
  Abstract: If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method for doing so. We then use the technique to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar. We discuss how this work provides a rebuttal to Fodor and Lepore's critique of Paul Churchland's state space semantics
  Additional links for this entry:
  http://www.laakshmi.com/aarre/cv/phipsy.pdf
  http://www.cs.ucsd.edu/groups/guru/docs/laakso2000.pdf
  http://www-cse.ucsd.edu/groups/guru/docs/laakso2000.pdf
  http://www.cse.ucsd.edu/groups/guru/docs/laakso2000.pdf
  http://www.informaworld.com/smpp/./ftinterface~content=a713690421~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/RY0JBTH3PY9B6GF2.pdf
  http://www.informaworld.com/index/RY0JBTH3PY9B6GF2.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2000/00000013/00000001/art00002
  
• Eric Lormand (ms). Connectionist content.   (More)
• Pete Mandik (2003). Varieties of representation in evolved and embodied neural networks.   (Abstract & more)
  Abstract:   In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem
  Additional links for this entry:
  http://citeseer.ist.psu.edu/mandik02varieties.html
  http://www.cogs.indiana.edu/cogx/Mandik_var_rep_03.pdf
  http://www.petemandik.com/philosophy/papers/varrep.pdf
  http://testweb.wpunj.edu/cohss/philosophy/FACULTY/mandik/papers/vreenn.pdf
  http://www.springerlink.com/content/m8023w0720l50p81/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5109389&PDF=1
  http://www.springerlink.com/index/M8023W0720L50P81.pdf
  http://www.ingentaconnect.com/content/klu/biph/2003/00000018/00000001/05109389
  
• Gerard O'Brien (1989). Connectionism, analogicity and mental content.   (Abstract & more)
  Abstract: In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed that connectionism adds to the traditional focus on computational processes, a new focus – one on the vehicles of mental representation, on the entities that carry content through the mind. Indeed, if Horgan and Tienson’s dynamical characterisation of connectionism is on the right track, then so intimate is the relationship between computational processes and representational vehicles, that connectionist cognitive science is committed to a resemblance theory of mental content
  Additional links for this entry:
  http://cogprints.org/1675/3/Connectionism_Analogicity_and_Mental_Content.pdf
  http://cogprints.org/1675/0/Connectionism_Analogicity_and_Mental_Content.pdf
  
• Gerard O'Brien & Jonathan Opie (2004). Notes toward a structuralist theory of mental representation.   (Abstract & more)
  Abstract: Any creature that must move around in its environment to find nutrients and mates, in order to survive and reproduce, faces the problem of sensorimotor control. A solution to this problem requires an on-board control mechanism that can shape the creature’s behaviour so as to render it “appropriate” to the conditions that obtain. There are at least three ways in which such a control mechanism can work, and Nature has exploited them all. The first and most basic way is for a creature to bump into the things in its environment, and then, depending on what has been encountered, seek to modify its behaviour accordingly. Such an approach is risky, however, since some things in the environment are distinctly unfriendly. A second and better way, therefore, is for a creature to exploit ambient forms of energy that carry information about the distal structure of the environment. This is an improvement on the first method since it enables the creature to respond to the surroundings without actually bumping into anything. Nonetheless, this second method also has its limitations, one of which is that the information conveyed by such ambient energy is often impoverished, ambiguous and intermittent
• William Ramsey (1997). Do connectionist representations earn their explanatory keep?   (More)
  Additional links for this entry:
  http://www.blackwell-synergy.com/doi/abs/10.1111/1468-0017.00035
  http://www.ingentaconnect.com/content/bpl/mila/1997/00000012/00000001/art00035
  http://www.ingentaconnect.com/content/bpl/mila/1997/00000012/00000001/art00003
  
• Joop Schopman & A. Shawky (1999). Remarks on the impact of connectionism on our thinking about concepts.   (More)
• Tony Stone & Martin Davies (2000). Autonomous psychology and the moderate neuron doctrine.   (Abstract & more)
  Abstract: _Two notions of autonomy are distinguished. The respective_ _denials that psychology is autonomous from neurobiology are neuron_ _doctrines, moderate and radical. According to the moderate neuron_ _doctrine, inter-disciplinary interaction need not aim at reduction. It is_ _proposed that it is more plausible that there is slippage from the_ _moderate to the radical neuron doctrine than that there is confusion_ _between the radical neuron doctrine and the trivial version._
  Additional links for this entry:
  http://philrsss.anu.edu.au/~mdavies/papers/aut.pdf
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=31694&jid=BBS&volumeId=22&issueId=05&aid=31693
  http://www.journals.cambridge.org/abstract_S0140525X99452197
  http://www.journals.cambridge.org/abstract_S0140525X99452197
  
• Evan Tiffany (1999). Semantics San Diego style.   (More)
  Additional links for this entry:
  http://links.jstor.org/sici?sici=0022-362X(199908)96:8<416:SSDS>2.0.CO;2-N
  http://www.jstor.org/sici?sici=0022-362X(199908)96:8<416:SSDS>2.0.CO;2-N
  http://www.jstor.org/stable/pdfplus/2564630.pdf
  
• Tim van Gelder (1999). Distributed vs. local representation.   (Abstract & more)
  Abstract: been to define various notions of distribution in terms of represented by one and the same distributed pattern (Mur- structures of correspondence between the represented items dock 1979). For example, it is standard in feedforward and the representational resources (e.g., van Gelder 1992). connectionist networks for one and the same set of synap- This approach may be misguided; the essence of this alter- tic weights to represent many associations between input native category of representation might be some other prop- and output. erty entirely. For example, Haugeland (1991) has suggested • Equipotentiality In some cases, an item is represented by
• Tim van Gelder (1990). Why distributed representation is inherently non-symbolic.   (Abstract & more)
  Abstract: There are many conflicting views concerning the nature of distributed representation, its compatibility or otherwise with symbolic representation, and its importance in characterizing the nature of connectionist models and their relationship to more traditional symbolic approaches to understanding cognition. Many have simply assumed that distribution is merely an implementation issue, and that symbolic mechanisms can be designed to take advantage of the virtues of distribution if so desired. Others, meanwhile, see the use of distributed representation as marking a fundamental difference between the two approaches. One reason for this diversity of opinion is the fact that the relevant notions - especially that of
• Jonathan A. Waskan (2001). A critique of connectionist semantics.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/WGXAHC9FUKFKEVME.pdf
  http://www.ingentaconnect.com/content/tandf/ccos/2001/00000013/00000003/art00004
  

Connectionism and Eliminativism

• Anthony Chemero (2007). Asking what's inside the head: Neurophilosophy meets the extended mind.   (Abstract & more)
  Abstract: In their historical overview of cognitive science, Bechtel, Abraham- son and Graham (1999) describe the field as expanding in focus be- ginning in the mid-1980s. The field had spent the previous 25 years on internalist, high-level GOFAI (“good old fashioned artificial intelli- gence” [Haugeland 1985]), and was finally moving “outwards into the environment and downards into the brain” (Bechtel et al, 1999, p.75). One important force behind the downward movement was Patricia Churchland’s Neurophilosophy (1986). This book began a movement bearing its name, one that truly came of age in 1999 when Kath- leen Akins won a million-dollar fellowship to begin the McDonnell Project in Philosophy and the Neurosciences. The McDonnell Project put neurophilosophy at the forefront of philosophy of mind and cogni- tive science, yielding proliferating articles, conferences, special journal issues and books. In two major new books, neurophilosophers Patricia Churchland (2002) and John Bickle (2003) clearly feel this newfound prominence: Churchland mocks those who do not apply findings in neuroscience to philosophical problems as “no-brainers”; Bickle mocks anyone with traditional philosophical concerns, including “naturalistic philosophers of mind” and other neurophilosophers
  Additional links for this entry:
  http://edisk.fandm.edu/tony.chemero/papers/bickchurchrev.pdf
  http://www.springerlink.com/content/m812557162027h08/fulltext.pdf
  
• M. Forster & Eric Saidel (1994). Connectionism and the fate of folk psychology.   (More)
  Additional links for this entry:
  http://philosophy.wisc.edu/forster/papers/Connection.pdf
  
• Gerard O'Brien (1991). Is connectionism commonsense?   (Abstract & more)
  Abstract: In this paper I critically examine the line of reasoning that has recently appeared in the literature that connects connectionism with eliminativism. This line of reasoning has it that if connectionist models turn out accurately to characterize our cognition, then beliefs, desires and the other intentional entities of commonsense psychology will be eliminated from our theoretical ontology. In complete contrast I argue (1) that not only is this line of reasoning mistaken about the eliminativist tendencies of connectionist models, but (2) that these models have the potential to provide a more robust vindication of commonsense psychology than classical computational models
  Additional links for this entry:
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a793921812~fulltext=713240930
  
• Paul G. Skokowski (ms). Belief in networks.   (More)
• Stephen P. Stich & Ted A. Warfield (1995). Reply to Clark and Smolensky: Do connectionist minds have beliefs?   (More)

The Connectionist/Classical Debate

• Frederick R. Adams, Kenneth Aizawa & Gary Fuller (1992). Rules in programming languages and networks.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=Gyik-KzGOmwC&oi=fnd&pg=PA49&ots=ZoWZXI2Brf&sig=mhv9JiNUtz2joCKf_iL9HXjfIOk
  
• Murat Aydede (1995). Connectionism and the language of thought.   (Abstract & more)
  Abstract: Fodor and Pylyshyn's (F&P) critique of connectionism has posed a challenge to connectionists: Adequately explain such nomological regularities as systematicity and productivity without postulating a "language of thought'' (LOT). Some connectionists declined to meet the challenge on the basis that the alleged regularities are somehow spurious. Some, like Smolensky, however, took the challenge very seriously, and attempted to meet it by developing models that are supposed to be non-classical
• Selmer Bringsjord (1991). Is the connectionist-logicist debate one of ai's wonderful red herrings?   (More)
  Additional links for this entry:
  http://kryten.mm.rpi.edu/connectionist_logicist_clash.pdf
  http://www.informaworld.com/index/776648445.pdf
  
• B. Chandrasekaran, A. Goel & D. Allemang (1988). Connectionism and information-processing abstractions.   (More)
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=1914723CI
  
• Wayne D. Christensen & Luca Tomassi (2006). Neuroscience in context: The new flagship of the cognitive sciences.   (More)
  Additional links for this entry:
  http://www.arts.adelaide.edu.au/humanities/people/philosophy/christensen_preprints/Christensen_Tommasi_2006_Neuroscience_in_context.pdf
  
• Martin Davies (1991). Concepts, connectionism, and the language of thought.   (Abstract & more)
  Abstract: The aim of this paper is to demonstrate a _prima facie_ tension between our commonsense conception of ourselves as thinkers and the connectionist programme for modelling cognitive processes. The language of thought hypothesis plays a pivotal role. The connectionist paradigm is opposed to the language of thought; and there is an argument for the language of thought that draws on features of the commonsense scheme of thoughts, concepts, and inference. Most of the paper (Sections 3-7) is taken up with the argument for the language of thought hypothesis. The argument for an opposition between connectionism and the language of thought comes towards the end (Section 8), along with some discussion of the potential eliminativist consequences (Sections 9 and
• Daniel C. Dennett (1991). Mother nature versus the walking encyclopedia.   (Abstract & more)
  Abstract: In 1982, Feldman and Ballard published "Connectionist models and their properties" in Cognitive Science , helping to focus attention on a family of similarly inspired research strategies just then under way, by giving the family a name: "connectionism." Now, seven years later, the connectionist nation has swelled to include such subfamilies as "PDP" and "neural net models." Since the ideological foes of connectionism are keen to wipe it out in one fell swoop aimed at its "essence", it is worth noting the diversity of not only the models but also the aspirations of the modelers. There is no good reason to suppose that they all pledge allegiance to any one principle..
  Additional links for this entry:
  http://ase.tufts.edu/cogstud/papers/motherna.htm
  http://cogprints.ecs.soton.ac.uk/archive/00000433/
  http://cogprints.ecs.soton.ac.uk/archive/00000433/00/motherna.htm
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:433
  
• Michael G. Dyer (1991). Connectionism versus symbolism in high-level cognition.   (More)
• Chris Eliasmith (2000). Is the brain analog or digital?   (Abstract & more)
  Abstract: It will always remain a remarkable phenomenon in the history of philosophy, that there was a time, when even mathematicians, who at the same time were philosophers, began to doubt, not of the accuracy of their geometrical propositions so far as they concerned space, but of their objective validity and the applicability of this concept itself, and of all its corollaries, to nature. They showed much concern whether a line in nature might not consist of physical points, and consequently that true space in the object might consist of simple [discrete] parts, while the space which the geometer has in his mind [being continuous] cannot be such
  Additional links for this entry:
  http://www.arts.uwaterloo.ca/~celiasmi/Papers/ce.2000.continuity.debate.csq.html
  
• Chris Eliasmith & Andy Clark (2002). Philosophical issues in brain theory and connectionism.   (Abstract & more)
  Abstract: In this article, we highlight three questions: (1) Does human cognition rely on structured internal representations? (2) How should theories, models and data relate? (3) In what ways might embodiment, action and dynamics matter for understanding the mind and the brain?
  Additional links for this entry:
  http://www.arts.uwaterloo.ca/~celiasmi/Papers/withclark.html
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/clarkeliasmith2.pdf
  
• Jerry A. Fodor & Zenon W. Pylyshyn (1988). Connectionism and cognitive architecture.   (Abstract & more)
  Abstract: This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=58067
  http://www.blutner.de/philom/connect/jaf.pdf
  http://ruccs.rutgers.edu/ftp/pub/papers/jaf.pdf
  http://portal.acm.org/citation.cfm?id=190704.190740
  http://www.ii.metu.edu.tr/~tekman/fodor&pylyshyn.pdf
  http://citeseer.ist.psu.edu/fodor88connectionism.html
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bLzaEHRW&sig=1PDJYThCtCF24M7R6UJK4T
  http://watarts.uwaterloo.ca/~celiasmi/courses/Phil256/papers/fodor.pylyshyn.1988.Connectionism cog architec.bbs.pdf
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bKr8GASZ&sig=lvbjst___yAcJlOgVzyRdzEV09k
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bGv5IAW0&sig=bfPrm5H3Z-QZFIh2WX-XvqgjqHE
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bIt7BHPT&sig=W9wLTgrsP59cPqZhSN0ysQbSVoM
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bHr6CFR-&sig=ic0bVVM1PfuvCnadV8OCRGBbNA4
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bGv5HHV-&sig=UkXTSX19hHzvmMUKvLT-lq0r50I
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKr7GJXU&sig=LBgHuNyf_KzzIi7ocvlxiFsIWIE
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bLtaAJQ0&sig=-rBwtgEcuMHfIKQJzOHWYIrBX9E
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKwbCDTZ&sig=00wE8Svuh3W8kqsUo0UKOYCfcAI
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bHx6HGX-&sig=5IusXYTvApV1_rtpQEkaBet0xn4
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKubBBQY&sig=HFGXyuxr_1mxH5ACFOe-zyaTw4o
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bIz7HEWT&sig=CAuCWiDdEmwTntCDA3H1Zyfn5a0
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bLzaEEXV&sig=e9QEFe5nD4bGAK5W4dDIRZBKNuM
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bIz7HCQU&sig=4TMgHlnpnDyehNFJT-NG470Bbz8
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bMvaHGVW&sig=9UEpXL5XYxZdQa3NvX7yU9iikyw
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bKx9AJPT&sig=7sjHbKdagqCd6sUT-Kk-fcITZlc
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bHx6HEPT&sig=9ypKOA18BrlaitMu_it-TVCBfUw
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKr7GFP0&sig=tdUqyz4ARusp_kokrKNOJedNknk
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bLtaAHPT&sig=cUrJjY2sKfVLW5Zp1ZtlEsdhcaM
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z8bJv8BJW0&sig=Qe8qR4Go6tnV95LlvTT6ELC_0Ds
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA3&ots=Z7kKwbCBVX&sig=iCTIyYq8EOvBdYu-srEXgyt-HoM
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O2dHfvmUIJ&sig=7hnYM8j4zdtpN4THjVSpzpGFEDs
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34InykTQC&sig=F0CONzdT49KnmoIp41iOngjBa_A
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34IhygWIA&sig=Zks6FRmma9XZGHW2EO1kvwIxesQ
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34DjtnWOI&sig=ohgYrPGd2Ut0_gQqvfJPXg0_xJo
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34FnvnRJB&sig=snID_Ptc5j-K-DxZvSjAScHkD6s
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34GjwhYPJ&sig=YnoEHTZY6rwzlL8DcfCj45uUQo0
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34JjynVOD&sig=XBIWzRchQULcgPTlX6HyBgOg_pQ
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34HfwmPLG&sig=35GDmDcPG526574Ar-pZaGlfYXA
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34ElunTIA&sig=OzlrEEZ2Td9CGt5mgUD99UXGCWE
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34HlxgYIA&sig=ZsqjFV8LrCGXI-3jf8Pliu_sVsc
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34FhvhWIA&sig=1yq1WHwwotr3qVSaqN0K4StIWc8
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O34EfuiUKI&sig=lDwkN-dMTxlLX_1XELpsD4ONtM4
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O2dHizhQJF&sig=s7d-fmzg598bYH-IpAMNrcyO8eo
  http://books.google.com/books?hl=en&lr=&id=ZLIAbDZzJ5sC&oi=fnd&pg=PA90&ots=O2dHkziQOE&sig=6AJnryXo_R8vj8qseWkK5DD31zQ
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=2450716&dopt=Citation
  
• Eric Lormand (1991). Classical and Connectionist Models.   (More)
• Eric Lormand (ms). Connectionist languages of thought.   (Abstract & more)
  Abstract: Fodor and Pylyshyn (1988) have presented an influential argument to the effect that any viable connectionist account of human cognition must implement a language of thought. Their basic strategy is to argue that connectionist models that do not implement a language of thought fail to account for the systematic relations among propositional attitudes. Several critics of the LOT hypothesis have tried to pinpoint flaws in Fodor and Pylyshyn’s argument (Smolensky 1989; Clark, 1989; Chalmers, 1990; Braddon-Mitchell and Fitzpatrick, 1990). One thing I will try to show is that the argument can be rescued from these criticisms. (Score: LOT 1, Visitors 0.) However, I agree that the argument fails, and I will provide a new account of how it goes wrong. (The score becomes tied.) Of course, the failure of Fodor and Pylyshyn’s argument does not mean that their conclusion is false. Consequently, some connectionist criticisms of Fodor and Pylyshyn’s article take the form of direct counterexamples to their conclusion (Smolensky 1989; van Gelder, 1990; Chalmers, 1990). I will argue, however, that Fodor and Pylyshyn’s conclusion survives confrontation with the alleged counterexamples. Finally, I provide an alternative argument that may succeed where Fodor and Pylyshyn’s fails. (Final Score: LOT 3, Visitors 1.)
• Susan Schneider (2009). The language of thought.   (Abstract & more)
  Abstract: According to the language of thought (or
• Susan Schneider (forthcoming). The nature of primitive symbols in the language of thought.   (Abstract & more)
  Abstract: This paper provides a theory of the nature of symbols in the language of thought (LOT). My discussion consists in three parts. In part one, I provide three arguments for the individuation of primitive symbols in terms of total computational role. The first of these arguments claims that Classicism requires that primitive symbols be typed in this manner; no other theory of typing will suffice. The second argument contends that without this manner of symbol individuation, there will be computational processes that fail to supervene on syntax, together with the rules of composition and the computational algorithms. The third argument says that cognitive science needs a natural kind that is typed by total computational role. Otherwise, either cognitive science will be incomplete, or its laws will have counterexamples. Then, part two defends this view from a criticism, offered by both Jerry Fodor and Jesse Prinz, who respond to my view with the charge that because the types themselves are individuated
  Additional links for this entry:
  http://www.sas.upenn.edu/~sls/documents/SchneiderNatureSymbolsMindLang.doc
  http://www.sas.upenn.edu/~sls/documents/SchneiderNatureSymbolsMindLang-1-2.pdf
  http://www.sas.upenn.edu/~sls/documents/Schneider-MindLangSymbols.pdf
  

Subsymbolic Computation

• Istvan S. N. Berkeley (2006). Moving the goal posts: A reply to Dawson and Piercey.   (Abstract & more)
  Abstract: Berkeley [Minds Machines 10 (2000) 1] described a methodology that showed the subsymbolic nature of an artificial neural network system that had been trained on a logic problem, originally described by Bechtel and Abrahamsen [Connectionism and the mind. Blackwells, Cambridge, MA, 1991]. It was also claimed in the conclusion of this paper that the evidence was suggestive that the network might, in fact, count as a symbolic system. Dawson and Piercey [Minds Machines 11 (2001) 197] took issue with this latter claim. They described some lesioning studies that they argued showed that Berkeley’s (2000) conclusions were premature. In this paper, these lesioning studies are replicated and it is shown that the effects that Dawson and Piercey rely upon for their argument are merely an artifact of a threshold function they chose to employ. When a threshold function much closer to that deployed in the original studies is used, the significant effects disappear
  Additional links for this entry:
  http://www.springerlink.com/content/g46288063247n5nv/fulltext.pdf
  http://www.springerlink.com/index/G46288063247N5NV.pdf
  
• Istvan S. N. Berkeley (2000). What the #$*%! Is a subsymbol?   (Abstract & more)
  Abstract:   In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic'' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky''s view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer the potential to provide a detailed, empirically based answer to questions about the nature of subsymbols. In this paper, a network analysis procedure and the results obtained using it are discussed. This provides the basis for an insight into the nature of subsymbols, which is surprising
  Additional links for this entry:
  http://www.ucs.louisiana.edu/~isb9112/dept/phil341/subsymbol/subsymbol.html
  http://www.springerlink.com/content/content/g81p084q505n1210/fulltext.pdf
  http://www.springerlink.com/content/g81p084q505n1210/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=258824&PDF=1
  http://www.springerlink.com/index/G81P084Q505N1210.pdf
  http://www.ingentaconnect.com/content/klu/mind/2000/00000010/00000001/00258824
  
• David J. Chalmers (1992). Subsymbolic computation and the chinese room.   (Abstract & more)
  Abstract: More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power
  Additional links for this entry:
  http://consc.net/papers/subsymbolic.pdf
  http://citeseer.ist.psu.edu/chalmers92subsymbolic.html
  http://www.u.arizona.edu/~chalmers/papers/subsymbolic.pdf
  
• Axel Cleeremans (1998). The other hard problem: How to bridge the gap between subsymbolic and symbolic cognition.   (Abstract & more)
  Abstract: The constructivist notion that features are purely functional is incompatible with the classical computational metaphor of mind. I suggest that the discontent expressed by Schyns, Goldstone and Thibaut about fixed-features theories of categorization reflects the growing impact of connectionism, and show how their perspective is similar to recent research on implicit learning, consciousness, and development. A hard problem remains, however: How to bridge the gap between subsymbolic and symbolic cognition
  Additional links for this entry:
  http://srsc.ulb.ac.be/axcWWW/papers/BBS-schyns98.html
  http://srsc.ulb.ac.be/axcwww/papers/pdf/98-C-on-SGT.pdf
  http://srsc.ulb.ac.be/axcWWW/papers/pdf/98-C-on-SGT.pdf
  http://srsc.ulb.ac.be/axcWWW/papers/pdf/98-C-on-SGT.pdf
  http://srsc-mac1.ulb.ac.be/axcWWW/papers/pdf/98-C-on-SGT.pdf
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=30266&jid=BBS&volumeId=21&issueId=01&aid=30265
  http://www.journals.cambridge.org/abstract_S0140525X98290108
  
• Paul Smolensky (1988). On the proper treatment of connectionism.   (More)

Philosophy of Connectionism, Misc

• William P. Bechtel & Adele A. Abrahamsen (1992). Connectionism and the future of folk psychology.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=k9SWKieq6-gC&oi=fnd&pg=PA340&ots=p7B1pO-v5y&sig=QFXNQv5o0oQLrXrlzp1rPvcShxo
  
• William P. Bechtel (1996). What should a connectionist philosophy of science look like?   (Abstract & more)
  Abstract: The reemergence of connectionism² has profoundly altered the philosophy of mind. Paul Churchland has argued that it should equally transform the philosophy of science. He proposes that connectionism offers radical and useful new ways of understanding theories and explanations
  Additional links for this entry:
  http://citeseer.ist.psu.edu/134759.html
  http://mechanism.ucsd.edu/~bill/research/CHURCHLA.pdf
  
• Istvan S. N. Berkeley (ms). A revisionist history of connectionism.   (Abstract & more)
  Abstract: According to the standard (recent) history of connectionism (see for example the accounts offered by Hecht-Nielsen (1990: pp. 14-19) and Dreyfus and Dreyfus (1988), or Papert's (1988: pp. 3-4) somewhat whimsical description), in the early days of Classical Computational Theory of Mind (CCTM) based AI research, there was also another allegedly distinct approach, one based upon network models. The work on network models seems to fall broadly within the scope of the term 'connectionist' (see Aizawa 1992), although the term had yet to be coined at the time. These two approaches were "two daughter sciences" according to Papert (1988: p. 3). The fundamental difference between these two 'daughters', lay (according to Dreyfus and Dreyfus (1988: p. 16)) in what they took to be the paradigm of intelligence. Whereas the early connectionists took learning to be fundamental, the traditional school concentrated upon problem solving
• István S. N. Berkeley (online). Some myths of connectionism.   (Abstract & more)
  Abstract: Since the emergence of what Fodor and Pylyshyn (1988) call 'new connectionism', there can be little doubt that connectionist research has become a significant topic for discussion in the Philosophy of Cognitive Science and the Philosophy of Mind. In addition to the numerous papers on the topic in philosophical journals, almost every recent book in these areas contain at least a brief reference to, or discussion of, the issues raised by connectionist research (see Sterelny 1990, Searle, 1992, and O Nualláin, 1995, for example). Other texts have focused almost exclusively upon connectionist issues (see Clark, 1993, Bechtel and Abrahamsen, 1991 and Lloyd, 1989, for example). Regrettably the discussions of connectionism found in the philosophical literature suffer from a number of deficiencies. My purpose in this paper is to highlight one particular problem and attempt to take a few steps to remedy the situation
• Istvan S. N. Berkeley (online). What is connectionism?   (Abstract & more)
  Abstract: Connectionism is a style of modeling based upon networks of interconnected simple processing devices. This style of modeling goes by a number of other names too. Connectionist models are also sometimes referred to as 'Parallel Distributed Processing' (or PDP for short) models or networks.1 Connectionist systems are also sometimes referred to as 'neural networks' (abbreviated to NNs) or 'artificial neural networks' (abbreviated to ANNs). Although there may be some rhetorical appeal to this neural nomenclature, it is in fact misleading as connectionist networks are commonly significantly dissimilar to neurological systems. For this reason, I will avoid using this terminology, other than in direct quotations. Instead, I will follow the practice I have adopted above and use 'connectionist' as my primary term for systems of this kind
• Susan J. Blackmore (2003). The case of the mysterious mind: Review of Radiant Cool, by Dan Lloyd.   (More)
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=250536AN
  
• Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural representation and neural computation.   (More)
  Additional links for this entry:
  http://links.jstor.org/sici?sici=1520-8583(1990)4<343:NRANC>2.0.CO;2-P
  http://www.jstor.org/sici?sici=1520-8583(1990)4<343:NRANC>2.0.CO;2-P
  http://www.jstor.org/stable/pdfplus/2214198.pdf
  
• Paul M. Churchland (1989). On the nature of explanation: A PDP approach.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=87498.87581
  http://adsabs.harvard.edu/abs/1990PhyD...42..281C
  http://books.google.com/books?hl=en&lr=&id=LqszY4kBw98C&oi=fnd&pg=PA75&ots=qXgwkOTy04&sig=yMyj0EhkKs802wSnrOjoUyDdztg
  
• Andy Clark (1990). Connectionism, competence and explanation.   (Abstract & more)
  Abstract: A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some equivalence class of algorithms/processing strategies so that the common properties highlighted by the chosen class may feature in psychologically interesting accounts. A question arises concerning the type of relation a theorist might expect to hold between such a competence model and a psychologically real processing strategy. Classical work in cognitive science expects the actual processing to depend on explicit or tacit knowledge of the competence theory. Connectionist work, for reasons to be explained, represents a departure from this norm. But the precise way in which a connectionist approach may disturb the satisfying classical symmetry of competence and processing has yet to be properly specified. A standard ?Newtonian? connectionist account, due to Paul Smolensky, is discussed and contrasted with a somewhat different ?rogue? account. A standard connectionist understanding has it that a classical competence theory describes an idealized subset of a network's behaviour. But the network's behaviour is not to be explained by its embodying explicit or tacit knowledge of the information laid out in the competence theory. A rogue model, by contrast, posits either two systems, or two aspects of a single system, such that one system does indeed embody the knowledge laid out in the competence theory
  Additional links for this entry:
  http://www.jstor.org/sici?sici=0007-0882(199006)41:2<195:CCAE>2.0.CO;2-U
  http://bjps.oxfordjournals.org/cgi/content/abstract/41/2/195
  http://bjps.oxfordjournals.org/cgi/reprint/41/2/195
  http://www.jstor.org/stable/pdfplus/687772.pdf
  
• Andy Clark (1989). Microfunctionalism: Connectionism and the Scientific Explanation of Mental States.   (Abstract & more)
  Abstract: This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999)
  Additional links for this entry:
  http://www.cogs.indiana.edu/andy/microfx.pdf
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/microfx.pdf
  
• Andy Clark & Chris Eliasmith (2002). Philosophical issues in brain theory and connectionism.   (More)
  Additional links for this entry:
  http://watarts.uwaterloo.ca/~celiasmi/Papers/withclark.html
  
• Mark Collier (1999). Filling the Gaps: Hume and Connectionism on the Continued Existence of Unperceived Objects".   (More)
• Jack Copeland (1996). On Alan Turing's anticipation of connectionism.   (Abstract & more)
  Abstract:   It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the network and the training process by means of a computer program. We outline Turing's connectionist project of 1948
  Additional links for this entry:
  http://citeseer.ist.psu.edu/copeland96alan.html
  http://www.springerlink.com/content/v3571g00l3504874/fulltext.pdf
  http://www.springerlink.com/index/V3571G00L3504874.pdf
  
• Robert F. Hadley (1999). Connectionism and novel combinations of skills: Implications for cognitive architecture.   (Abstract & more)
  Abstract:   In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just not plausible to suppose that our brains are digital computers. Thus, they could not support a classical architecture. I argue here for a middle ground between connectionism and classicism. I assume, for argument's sake, that some form(s) of connectionism can provide reasonably approximate models – at least for lower-level cognitive processes. Given this assumption, I argue on theoretical and empirical grounds that most human mental skills must reside in separate connectionist modules or sub-networks. Ultimately, it is argued that the basic tenets of connectionism, in conjunction with the fact that humans often employ novel combinations of skill modules in rule following and problem solving, lead to the plausible conclusion that, in certain domains, high level cognition requires some form of classical architecture. During the course of argument, it emerges that only an architecture with classical structure could support the novel patterns of information flow and interaction that would exist among the relevant set of modules. Such a classical architecture might very well reside in the abstract levels of a hybrid system whose lower-level modules are purely connectionist
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=596712.596817
  http://www.springerlink.com/content/l64416n38j41q278/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=206285&PDF=1
  http://www.springerlink.com/index/L64416N38J41Q278.pdf
  http://www.ingentaconnect.com/content/klu/mind/1999/00000009/00000002/00206285
  
• Gary Hatfield (1990). Gibsonian representations and connectionist symbol-processing: Prospects for unification.   (More)
• David Kirsh (1992). PDP Learnability and Innate Knowledge of Language.   (Abstract & more)
  Abstract: It is sometimes argued that if PDP networks can be trained to make correct judgements of grammaticality we have an existence proof that there is enough information in the stimulus to permit learning grammar by inductive means alone. This seems inconsistent superficially with Gold's theorem and at a deeper level with the fact that networks are designed on the basis of assumptions about the domain of the function to be learned. To clarify the issue I consider what we should learn from Gold's theorem, then go on to inquire into what it means to say that knowledge is domain specific. I first try sharpening the intuitive notion of domain specific knowledge by reviewing the alleged difference between processing limitatons due to shartage of resources vs shortages of knowledge. After rejecting different formulations of this idea, I suggest that a model is language specific if it transparently refer to entities and facts about language as opposed to entities and facts of more general mathematical domains. This is a useful but not necessary condition. I then suggest that a theory is domain specific if it belongs to a model family which is attuned in a law-like way to domain regularities. This leads to a comparison of PDP and parameter setting models of language learning. I conclude with a novel version of the poverty of stimulus argument.
• William G. Lycan (1991). Homuncular functionalism meets PDP.   (More)
• L. F. Niklasson & Tim van Gelder (online). Can connectionist models exhibit non-classical structure sensitivity?   (Abstract & more)
  Abstract: Department of Computer Science Philosophy Program, Research School of Social Sciences University of Skövde, S-54128, SWEDEN Australian National University, Canberra ACT 0200
  Additional links for this entry:
  http://citeseer.ist.psu.edu/niklasson94can.html
  
• Gerard O'Brien & Jonathan Opie (2002). Radical connectionism: Thinking with (not in) language.   (Abstract & more)
  Abstract: In this paper we defend a position we call radical connectionism. Radical connectionism claims that cognition _never_ implicates an internal symbolic medium, not even when natural language plays a part in our thought processes. On the face of it, such a position renders the human capacity for abstract thought quite mysterious. However, we argue that connectionism is committed to an analog conception of neural computation, and that representation of the abstract is no more problematic for a system of analog vehicles than for a symbol system. Natural language is therefore not required as a representational medium for abstract thought. Since natural language is arguably not a representational medium _at all_, but a conventionally governed scheme of communicative signals, we suggest that the role of internalised (i.e., self- directed) language is best conceived in terms of the coordination and control of cognitive activities within the brain
  Additional links for this entry:
  http://linkinghub.elsevier.com/retrieve/pii/S0271530902000101
  http://www.ingentaconnect.com/content/els/02715309/2002/00000022/00000003/art00010
  
• Gualtiero Piccinini (2007). Connectionist computation.   (Abstract & more)
  Abstract: The following three theses are inconsistent: (1) (Paradigmatic) connectionist systems perform computations. (2) Performing computations requires executing programs. (3) Connectionist systems do not execute programs. Many authors embrace (2). This leads them to a dilemma: either connectionist systems execute programs or they don't compute. Accordingly, some authors attempt to deny (1), while others attempt to deny (3). But as I will argue, there are compelling reasons to accept both (1) and (3). So, we should replace (2) with a more satisfactory account of computation. Once we do, we can see more clearly what is peculiar to connectionist computation.
• William Ramsey & Stephen P. Stich (1990). Connectionism and three levels of nativism.   (Abstract & more)
  Abstract:   Along with the increasing popularity of connectionist language models has come a number of provocative suggestions about the challenge these models present to Chomsky's arguments for nativism. The aim of this paper is to assess these claims. We begin by reconstructing Chomsky's argument from the poverty of the stimulus and arguing that it is best understood as three related arguments, with increasingly strong conclusions. Next, we provide a brief introduction to connectionism and give a quick survey of recent efforts to develop networks that model various aspects of human linguistic behavior. Finally, we explore the implications of this research for Chomsky's arguments. Our claim is that the relation between connectionism and Chomsky's views on innate knowledge is more complicated than many have assumed, and that even if these models enjoy considerable success the threat they pose for linguistic nativism is small
  Additional links for this entry:
  http://www.springerlink.com/content/p66n338hjq44332p/fulltext.pdf
  http://www.springerlink.com/index/P66N338HJQ44332P.pdf
  
• Barry Smith (1997). The connectionist mind: A study of Hayekian psychology.   (Abstract & more)
  Abstract: Introduction I shall begin my remarks with some discussion of recent work in cognitive science, and the participants in this meeting might find it useful to note that I might equally well have chosen as title of my paper something like 'Artificial Intelligence and the Free Market Order'. They might care to note also that I am, as far as the achievements and goals of research in artificial intelligence are concerned, something of a sceptic. My appeal to cognitive science in what follows is designed to serve clarificatory ends, and to raise new questions, of a sort which will become clear as the paper progresses
  Additional links for this entry:
  http://ontology.buffalo.edu/smith/articles/HAYEK.HTM
  http://wings.buffalo.edu/philosophy/faculty/smith/articles/HAYEK.HTM
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:306
  http://wings.buffalo.edu/academic/department/philosophy/faculty/smith/articles/HAYEK.HTM
  http://cogprints.org/306/1/connect.html
  http://cogprints.org/306/0/connect.html
  

Philosophy of Connectionism, Foundational Empirical Issues

• Kenneth Aizawa (1992). Connectionism and artificial intelligence: History and philosophical interpretation.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/777592363.pdf
  
• Istvan S. N. Berkeley (ms). Connectionism reconsidered: Minds, machines and models.   (Abstract & more)
  Abstract: In this paper the issue of drawing inferences about biological cognitive systems on the basis of connectionist simulations is addressed. In particular, the justification of inferences based on connectionist models trained using the backpropagation learning algorithm is examined. First it is noted that a justification commonly found in the philosophical literature is inapplicable. Then some general issues are raised about the relationships between models and biological systems. A way of conceiving the role of hidden units in connectionist networks is then introduced. This, in combination with an assumption about the way evolution goes about solving problems, is then used to suggest a means of justifying inferences about biological systems based on connectionist research
  Additional links for this entry:
  http://www.ucs.louisiana.edu/~isb9112/dept/phil341/conrecon/conrecon.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1975
  http://cogprints.org/1975/3/CReconF.pdf
  http://cogprints.org/1975/0/CReconF.pdf
  
• Andy Clark (1994). Representational trajectories in connectionist learning.   (Abstract & more)
  Abstract:   The paper considers the problems involved in getting neural networks to learn about highly structured task domains. A central problem concerns the tendency of networks to learn only a set of shallow (non-generalizable) representations for the task, i.e., to miss the deep organizing features of the domain. Various solutions are examined, including task specific network configuration and incremental learning. The latter strategy is the more attractive, since it holds out the promise of a task-independent solution to the problem. Once we see exactly how the solution works, however, it becomes clear that it is limited to a special class of cases in which (1) statistically driven undersampling is (luckily) equivalent to task decomposition, and (2) the dangers of unlearning are somehow being minimized. The technique is suggestive nonetheless, for a variety of developmental factors may yield the functional equivalent of both statistical AND informed undersampling in early learning
  Additional links for this entry:
  http://www.springerlink.com/content/content/r7812132kh676544/fulltext.pdf
  http://www.springerlink.com/content/r7812132kh676544/fulltext.pdf
  http://www.springerlink.com/index/R7812132KH676544.pdf
  
• Andy Clark & S. Thornton (1997). Trading spaces: Computation, representation, and the limits of uninformed learning.   (More)
  Additional links for this entry:
  http://www.bbsonline.org/documents/a/00/00/04/44/index.html
  http://www.bbsonline.org/Preprints/OldArchive/bbs.clark.html
  http://www.psych.nyu.edu/gary/marcusArticles/Marcus 1997 BBS.pdf
  http://www.denizyuret.com/ref/clark_andy/clark.trading-spaces.ps
  http://bbsonline.cup.cam.ac.uk/Preprints/OldArchive/bbs.clark.html
  http://neuron-ai.tuke.sk/~hudecm/d/TradeSpacesComputationRepresentationAndTheLimitsOfUninformedLearning_clark95trading.ps
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=29218&jid=BBS&volumeId=20&issueId=01&aid=29217
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=10096995&dopt=Citation
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&list_uids=10096995&dopt=Citation
  http://www.journals.cambridge.org/abstract_S0140525X97000022
  http://journals.cambridge.org/article_S0140525X97240021
  http://journals.cambridge.org/article_S0140525X97300028
  
• Andy Clark & Chris Thornton (1997). Relational learning re-examined.   (More)
• D. Cliff (1990). Computational Neuroethology: A Provisional Manifesto.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=116521
  http://citeseer.ist.psu.edu/cliff91computational.html
  
• James Franklin (1996). How a neural net grows symbols.   (Abstract & more)
  Abstract: Brains, unlike artificial neural nets, use sym- bols to summarise and reason about percep- tual input. But unlike symbolic AI, they “ground” the symbols in the data: the sym- bols have meaning in terms of data, not just meaning imposed by the outside user. If neu- ral nets could be made to grow their own sym- bols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as to combine the good features of each
• Susan Hanson & D. Burr (1990). What connectionist models learn.   (More)
• S. Kaplan, M. Weaver & Robert M. French (1990). Active symbols and internal models: Towards a cognitive connectionism.   (More)
  Additional links for this entry:
  http://www.springerlink.com/index/G674K4JTV61G1510.pdf
  
• J. Lachter & Thomas G. Bever (1988). The relation between linguistic structure and associative theories of language learning.   (More)
  Additional links for this entry:
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=3349761&dopt=Citation
  
• Raymond J. Nelson (1989). Philosophical issues in Edelman's neural darwinism.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/778705900.pdf
  
• Mike Oaksford, Nick Chater & Keith Stenning (1990). Connectionism, classical cognitive science and experimental psychology.   (More)
  Additional links for this entry:
  http://www.springerlink.com/index/L20P853057770738.pdf
  
• Gerard O'Brien (1998). The role of implementation in connectionist explanation.   (More)
  Additional links for this entry:
  http://psycprints.ecs.soton.ac.uk/archive/00000555/
  http://psycprints.ecs.soton.ac.uk/archive/00000555/#html
  
• Steven Pinker & Alan Prince (1988). On language and connectionism.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=58068
  http://cogsci.soton.ac.uk/harnad/Papers/Py104/pinker.conn.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Py104/pinker.conn.html
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z7kKwcEIWV&sig=Eg_CAnM1Mg4n2ZmvA2OlpN9m9sw
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bLtbEAR0&sig=v5UAf_38SNcEICx0yEG3cIhTzJo
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z7kKr8IIX_&sig=88u9kHQCTGaVdnM7m8iNb2PPKTY
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bLzbIGYY&sig=4kAym1qCPEcgjt8JlqshIRKhIDU
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bKrazHW0&sig=xDNu_Z09OZAy27O-n3WfUmMLQUs
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bMvcCAYZ&sig=5sN8KnPgqLSBs0KApMSatX82EJg
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bKxaDHUT&sig=KeLD5m7m1-u6IfRCZkbe94UL1Es
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bHx8AFUW&sig=cRtyKLds-QhYetV0FyVJPWnziVI
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z7kKucDIYZ&sig=E7BrmcOGPhpsKDfBsYC2IVCxHn0
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bHr7GBXY&sig=HMW-DysLVZiCJI1ORuFT8UdXKHM
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bIt8FJUY&sig=ydGhKMgRwpBcRx4at94y5lYz9mE
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bGv7AITY&sig=rWgaifux53r8Vm_6Rj2-68EJMjE
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bJv9EARU&sig=SVeTbYG1MwT2L1atC0yVUgMx0ys
  http://books.google.com/books?hl=en&lr=&id=vZ1t2AnZy3UC&oi=fnd&pg=PA73&ots=Z8bIz9AJU0&sig=-djAWMRY1P5GSdsHAJzC8nqpsAY
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=2450717&dopt=Citation
  

Special Topics in AI

• Kris Rhodes (ms). Vindication of the Rights of Machine.   (Abstract & more)
  Abstract: In this paper, I argue that certain Machines can have rights independently of whether they are sentient, or conscious, or whatever you might call it.
  Additional links for this entry:
  http://www.igradeyourpaper.com/VRMv3.doc
  

• Nick Bostrom (ms). Ethical issues in advanced artificial intelligence.   (Abstract & more)
  Abstract: The ethical issues related to the possible future creation of machines with general intellectual capabilities far outstripping those of humans are quite distinct from any ethical problems arising in current automation and information systems. Such superintelligence would not be just another technological development; it would be the most important invention ever made, and would lead to explosive progress in all scientific and technological fields, as the superintelligence would conduct research with superhuman efficiency. To the extent that ethics is a cognitive pursuit, a superintelligence could also easily surpass humans in the quality of its moral thinking. However, it would be up to the designers of the superintelligence to specify its original motivations. Since the superintelligence may become unstoppably powerful because of its intellectual superiority and the technologies it could develop, it is crucial that it be provided with human-friendly motivations. This paper surveys some of the unique ethical issues in creating superintelligence, and discusses what motivations we ought to give a superintelligence, and introduces some cost-benefit considerations relating to whether the development of superintelligent machines ought to be accelerated or retarded
  Additional links for this entry:
  http://www.nickbostrom.com/ethics/ai.html
  
• Nick Bostrom (1998). How long before superintelligence?   (Abstract & more)
  Abstract: _This paper outlines the case for believing that we will have superhuman artificial intelligence_ _within the first third of the next century. It looks at different estimates of the processing power of_ _the human brain; how long it will take until computer hardware achieve a similar performance;_ _ways of creating the software through bottom-up approaches like the one used by biological_ _brains; how difficult it will be for neuroscience figure out enough about how brains work to_ _make this approach work; and how fast we can expect superintelligence to be developed once_ _there is human-level artificial intelligence._
• Nick Bostrom (2003). Taking intelligent machines seriously: Reply to critics.   (Abstract & more)
  Abstract: In an earlier paper in this journal[1], I sought to defend the claims that (1) substantial probability should be assigned to the hypothesis that machines will outsmart humans within 50 years, (2) such an event would have immense ramifications for many important areas of human concern, and that consequently (3) serious attention should be given to this scenario. Here, I will address a number of points made by several commentators
  Additional links for this entry:
  http://www.questia.com/PM.qst?a=o&se=gglsc&d=5002030442
  http://linkinghub.elsevier.com/retrieve/pii/S0016328703000466
  http://www.ingentaconnect.com/content/els/00163287/2003/00000035/00000008/art00046
  
• Nick Bostrom (ms). When machines outsmart humans.   (Abstract & more)
  Abstract: Artificial intelligence is a possibility that should not be ignored in any serious thinking about the future, and it raises many profound issues for ethics and public policy that philosophers ought to start thinking about. This article outlines the case for thinking that human-level machine intelligence might well appear within the next half century. It then explains four immediate consequences of such a development, and argues that machine intelligence would have a revolutionary impact on a wide range of the social, political, economic, commercial, technological, scientific and environmental issues that humanity will face over the coming decades
• David J. Chalmers, The singularity: A philosophical analysis.   (Abstract & more)
  Abstract: What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. So if it is itself designed by humans, it will be capable of designing a machine more intelligent than itself. By similar reasoning, this next machine will also be capable of designing a machine more intelligent than itself. If every machine in turn does what it is capable of, we should expect a sequence of ever more intelligent machines. This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do so indefinitely. Now suppose that we have human-level artificial intelligence 1 designing new processors. Then faster processing will lead to faster designers and an ever-faster design cycle, leading to a limit point soon afterwards. The argument for a speed explosion was set out by the artificial intelligence researcher Ray Solomonoff in his 1985 article “The Time Scale of Artificial Intelligence”.1 Eliezer Yudkowsky gives a succinct version of the argument in his 1996 article “Staring at the Singularity”: “Computing speed doubles every two subjective years of work..
• Robin Hanson, Is a singularity just around the corner?   (Abstract & more)
  Abstract: Economic growth is determined by the supply and demand of investment capital; technology determines the demand for capital, while human nature determines the supply. The supply curve has two distinct parts, giving the world economy two distinct modes. In the familiar slow growth mode, rates of return are limited by human discount rates. In the fast growth mode, investment is limited by the world's wealth. Historical trends suggest that we may transition to the fast mode in roughly another century and a half
• Vernor Vinge (online). The technological singularity.   (More)
  Additional links for this entry:
  http://wholeearthmag.com/ArticleBin/111-3.pdf
  http://adsabs.harvard.edu/abs/1993vise.nasa...11V
  http://www.aleph.se/Trans/Global/Singularity/sing.html
  http://www.cs.ucsd.edu/users/goguen/misc/singularity.html
  http://www.kurzweilai.net/articles/art0092.html?printable=1
  http://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=N9427359AH
  

• David J. Chalmers, The singularity: A philosophical analysis.   (Abstract & more)
  Abstract: What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. So if it is itself designed by humans, it will be capable of designing a machine more intelligent than itself. By similar reasoning, this next machine will also be capable of designing a machine more intelligent than itself. If every machine in turn does what it is capable of, we should expect a sequence of ever more intelligent machines. This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do so indefinitely. Now suppose that we have human-level artificial intelligence 1 designing new processors. Then faster processing will lead to faster designers and an ever-faster design cycle, leading to a limit point soon afterwards. The argument for a speed explosion was set out by the artificial intelligence researcher Ray Solomonoff in his 1985 article “The Time Scale of Artificial Intelligence”.1 Eliezer Yudkowsky gives a succinct version of the argument in his 1996 article “Staring at the Singularity”: “Computing speed doubles every two subjective years of work..

Cyborgs

Transhumanism

Cybernetics

• Glen Mazis (2008). Cyborg Life: The In-Between of Humans and Machines.   (More)

Dynamical Systems

• Adele A. Abrahamsen & William P. Bechtel (2006). Phenomena and mechanisms: Putting the symbolic, connectionist, and dynamical systems debate in broader perspective.   (Abstract & more)
  Abstract: Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved:

  • Just how modular is the mind? (section 1) – a debate initially pitting encapsulated
  mechanisms (Fodorian modules that feed their ultimate outputs to a nonmodular central
  cognition) against highly interactive ones (e.g., connectionist networks that continuously
  feed streams of output to one another).
  • Does the mind process language-like representations according to formal rules? (this
  section) – a debate initially pitting symbolic architectures (such as Chomsky’s generative
  grammar or Fodor’s language of thought) against less language-like architectures (such
  as connectionist or dynamical ones).

  Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language processing—involves rules operating on language-like representations actually predates cognitive science. In traditional philosophy of mind, mental life is construed as involving propositional attitudes—that is, such attitudes towards propositions as believing, fearing, and desiring that they be true—and logical inferences from them. On this view, if a person desires that a proposition be true and believes that if she performs a certain action it will become true, she will make the inference and (absent any overriding consideration) perform the action
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=OR-ApDfUMUkC&oi=fnd&pg=RA1-PT103&dq=Phenomena+and+mechanisms+Abrahamsen&ots=LNjBNEN5rD&sig=BPLpA8As8Lm_kuVh5kiz4L2Xxuc
  
• William P. Bechtel (online). Dynamics and decomposition: Are they compatible?   (Abstract & more)
  Abstract: Much of cognitive neuroscience as well as traditional cognitive science is engaged in a quest for mechanisms through a project of decomposition and localization of cognitive functions. Some advocates of the emerging dynamical systems approach to cognition construe it as in opposition to the attempt to decompose and localize functions. I argue that this case is not established and rather explore how dynamical systems tools can be used to analyze and model cognitive functions without abandoning the use of decomposition and localization to understand mechanisms of cognition
• William P. Bechtel (1998). Representations and cognitive explanations: Assessing the dynamicist challenge in cognitive science.   (Abstract & more)
  Abstract: Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations
  Additional links for this entry:
  http://www.leaonline.com/doi/abs/10.1207/s15516709cog2203_2
  http://www.leaonline.com/doi/pdfplus/10.1207/s15516709cog2203_2
  http://www.cogsci.rpi.edu/CSJarchive/1998v22/i03/p0295p0318/MAIN.PDF
  http://www.ingentaconnect.com/content/els/03640213/1998/00000022/00000003/art80042
  
• Mark A. Bedau (1997). Emergent models of supple dynamics in life and mind.   (Abstract & more)
  Abstract: The dynamical patterns in mental phenomena have a characteristic suppleness&emdash;a looseness or softness that persistently resists precise formulation&emdash;which apparently underlies the frame problem of artificial intelligence. This suppleness also undermines contemporary philosophical functionalist attempts to define mental capacities. Living systems display an analogous form of supple dynamics. However, the supple dynamics of living systems have been captured in recent artificial life models, due to the emergent architecture of those models. This suggests that analogous emergent models might be able to explain supple dynamics of mental phenomena. These emergent models of the supple mind, if successful, would refashion the nature of contemporary functionalism in the philosophy of mind
  Additional links for this entry:
  http://people.reed.edu/~mab/publications/papers/emergent-models.html
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9209753&dopt=Citation
  http://www.ingentaconnect.com/content/ap/br/1997/00000034/00000001/art00904
  
• Anthony Chemero (2000). Anti-representationalism and the dynamical stance.   (Abstract & more)
  Abstract: Arguments in favor of anti-representationalism in cognitive science often suffer from a lack of attention to detail. The purpose of this paper is to fill in the gaps in these arguments, and in so doing show that at least one form of anti- representationalism is potentially viable. After giving a teleological definition of representation and applying it to a few models that have inspired anti- representationalist claims, I argue that anti-representationalism must be divided into two distinct theses, one ontological, one epistemological. Given the assumptions that define the debate, I give reason to think that the ontological thesis is false. I then argue that the epistemological thesis might, in the end, turn out to be true, despite a potentially serious difficulty. Along the way, there will be a brief detour to discuss a controversy from early twentieth century physics
  Additional links for this entry:
  http://links.jstor.org/sici?sici=0031-8248(200012)67:4<625:AATDS>2.0.CO;2-Y
  http://www.jstor.org/sici?sici=0031-8248(200012)67:4<625:AATDS>2.0.CO;2-Y
  http://www.journals.uchicago.edu/cgi-bin/resolve?id=doi:10.1086/392858
  http://www.jstor.org/stable/pdfplus/188710.pdf
  
• Tony Chemero (2001). Dynamical explanation and mental representations.   (Abstract & more)
  Abstract: Markman and Dietrich¹ recently recommended extending our understanding of representation to incorporate insights from some “alternative” theories of cognition: perceptual symbol systems, situated action, embodied cognition, and dynamical systems. In particular, they suggest that allowances be made for new types of representation which had been previously under-emphasized in cognitive science. The amendments they recommend are based upon the assumption that the alternative positions each agree with the classical view that cognition requires representations, internal mediating states that bear information.² In the case of one of the alternatives, dynamical systems³, this is simply false: many dynamically-oriented cognitive scientists are anti-representationalists.^4,5,6
  Additional links for this entry:
  http://edisk.fandm.edu/tony.chemero/papers/mand.pdf
  http://citeseer.ist.psu.edu/chemero01dynamical.html
  http://linkinghub.elsevier.com/retrieve/pii/S1364661300016272
  http://www.ingentaconnect.com/content/els/13646613/2001/00000005/00000004/art01627
  
• Anthony Chemero & William Cordeiro (online). Dynamical, ecological sub-persons.   (Abstract & more)
  Abstract: Scientific and Philosophical Studies of Mind Franklin and Marshall College Lancaster, PA 17604-3003 USA
• Andy Clark (online). Commentary on "the modularity of dynamic systems".   (Abstract & more)
  Abstract: 1. Throughout the paper, and especially in the section called "LISP vs. DST", I worried that there was not enough focus on EXPLANATION. For the real question, it seems to me, is not whether some dynamical system can implement human cognition, but whether the dynamical description of the system is more explanatorily potent than a computational/representational one. Thus we know, for example, that a purely physical specification can fix a system capable of computing any LISP function. But from this it doesn't follow that the physical description is the one we need to understand the power of the system considered as an information processing device. In the same way, I don't think your demonstration that bifurcating attractor sets can yield the same behavior as a LISP program goes any way towards showing that we should not PREFER the LISP description. To reduce symbolic stories to a subset of DST (as hinted in that section) requires MORE than showing this kind of equivalence: it requires showing that there is explanatory gain, or at the very least, no explanatory loss, at that level. I append an extract from a recent paper of mine that touches on these issues, in case it helps clarify what I am after here
  Additional links for this entry:
  http://users.california.com/~mcmf/clark.html
  
• Andy Clark (1998). Time and mind.   (Abstract & more)
  Abstract: Mind, it has recently been argued1, is a thoroughly temporal phenomenon: so temporal, indeed, as to defy description and analysis using the traditional computational tools of cognitive scientific understanding. The proper explanatory tools, so the suggestion goes, are instead the geometric constructs and differential equations of Dynamical Systems Theory. I consider various aspects of the putative temporal challenge to computational understanding, and show that the root problem turns on the presence of a certain kind of causal web: a web that involves multiple components (both inner and outer) linked by chains of continuous and reciprocal causal influence. There is, however, no compelling route from such facts about causal and temporal complexity to the radical anti- computationalist conclusion. This is because, interactive complexities notwithstanding, the computational approach provides a kind of explanatory understanding that cannot (I suggest) be recreated using the alternative resources of pure Dynamical Systems Theory. In particular, it provides a means of mapping information flow onto causal structure -- a mapping that is crucial to understanding the distinctive kinds of flexibility and control characteristic of truly mindful engagements with the world. Where we confront especially complex interactive causal webs, however, it does indeed become harder to isolate the syntactic vehicles required by the computational approach. Dynamical Systems Theory, I conclude, may play a vital role in recovering such vehicles from the burgeoning mass of real-time interactive complexity
  Additional links for this entry:
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/time.pdf
  http://links.jstor.org/sici?sici=0022-362X(199807)95:7<354:TAM>2.0.CO;2-E
  http://www.jstor.org/sici?sici=0022-362X(199807)95:7<354:TAM>2.0.CO;2-E
  http://www.jstor.org/stable/pdfplus/2564539.pdf
  
• Joe Cruz (online). Psychological explanation and noise in modeling. Comments on Whit Schonbein's "cognition and the power of continuous dynamical systems".   (Abstract & more)
  Abstract: I find myself ambivalent with respect to the line of argument that Schonbein offers. I certainly want to acknowledge and emphasize at the outset that Schonbein’s discussion has brought to the fore a number of central, compelling and intriguing issues regarding the nature of the dynamical approach to cognition. Though there is much that seems right in this essay, perhaps my view is that the paper invites more questions than it answers. My remarks here then are in the spirit of scouting some of the surrounding terrain in order to see just what Schonbein’s claim is and what arguments or options may be open to the dynamicist
• Chris Eliasmith (2001). Attractive and in-discrete: A critique of two putative virtues of the dynamicist theory of mind.   (Abstract & more)
  Abstract:   I argue that dynamicism does not provide a convincing alternative to currently available cognitive theories. First, I show that the attractor dynamics of dynamicist models are inadequate for accounting for high-level cognition. Second, I argue that dynamicist arguments for the rejection of computation and representation are unsound in light of recent empirical findings. This new evidence provides a basis for questioning the importance of continuity to cognitive function, challenging a central commitment of dynamicism. Coupled with a defense of current connectionist theory, these two critiques lead to the conclusion that dynamicists have failed to achieve their goal of providing a new paradigm for understanding cognition
  Additional links for this entry:
  http://watarts.uwaterloo.ca/~celiasmi/Papers/ce.press.attractive.mm.html
  http://www.springerlink.com/content/w7777h0413p32864/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=338891&PDF=1
  http://www.springerlink.com/index/W7777H0413P32864.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000003/00338891
  
• Chris Eliasmith (1997). Computation and dynamical models of mind.   (Abstract & more)
  Abstract:   Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical exemplar of the class of systems necessary for cognition and MOT is an empirical instantiation of that class. However, I shall argue that neither the Watt governor nor MOT successfully fulfill these prescribed roles. This failure, along with van Gelder's peculiar use of the concept of computation and his struggle with representationalism, prevent him from providing a convincing alternative to current cognitive theories
  Additional links for this entry:
  http://cogprints.org/325/
  http://cogprints.ecs.soton.ac.uk/archive/00000325/
  http://cogprints.ecs.soton.ac.uk/archive/00000325/
  http://portal.acm.org/citation.cfm?id=596707.596756
  http://www.arts.uwaterloo.ca/~celiasmi/Papers/dynamics.mm.html
  http://www.arts.uwaterloo.ca/~celiasmi/Papers/dynamics.mm.html
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=316239CI
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:325
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=316239CI
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:325
  http://www.springerlink.com/content/content/u013j75041272360/fulltext.pdf
  http://www.springerlink.com/content/u013j75041272360/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=125137&PDF=1
  http://www.kluweronline.com/article.asp?PIPS=125137&PDF=1
  http://www.springerlink.com/index/U013J75041272360.pdf
  http://www.springerlink.com/index/U013J75041272360.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000004/00125137
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000004/00125137
  http://cogprints.org/325/1/dynamics.mm.html
  http://cogprints.org/325/0/dynamics.mm.html
  
• Chris Eliasmith (1998). Dynamical models and Van gelder's dynamicism.   (Abstract & more)
  Abstract: Van Gelder has presented a position which he ties closely to a broad class of models known as dynamical models. While supporting many of his broader claims about the importance of this class (as has been argued by connectionists for quite some time), I note that there are a number of unique characteristics of his brand of dynamicism. I suggest that these characteristics engender difficulties for his view
  Additional links for this entry:
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=30438&jid=BBS&volumeId=21&issueId=05&aid=30437
  http://www.journals.cambridge.org/abstract_S0140525X98341734
  
• Chris Eliasmith (2003). Moving beyond metaphors: Understanding the mind for what it is.   (More)
  Additional links for this entry:
  http://www.arts.uwaterloo.ca/~doneill/cogsci600/Eliasmith.pdf
  http://www.arts.uwaterloo.ca/~raha/CogSci600_web/Readings/eliasmith1.pdf
  http://watarts.uwaterloo.ca/~celiasmi/Papers/eliasmith.moving beyond metaphors.jphil.pdf
  http://www.arts.uwaterloo.ca/~celiasmi/Papers/eliasmith.moving beyond metaphors.jphil.pdf
  
• Chris Eliasmith (1996). The third contender: A critical examination of the dynamicist theory of cognition.   (Abstract & more)
  Abstract: In a recent series of publications, dynamicist researchers have proposed a new conception of cognitive functioning. This conception is intended to replace the currently dominant theories of connectionism and symbolicism. The dynamicist approach to cognitive modeling employs concepts developed in the mathematical field of dynamical systems theory. They claim that cognitive models should be embedded, low-dimensional, complex, described by coupled differential equations, and non-representational. In this paper I begin with a short description of the dynamicist project and its role as a cognitive theory. Subsequently, I determine the theoretical commitments of dynamicists, critically examine those commitments and discuss current examples of dynamicist models. In conclusion, I determine dynamicism's relation to symbolicism and connectionism and find that the dynamicist goal to establish a new paradigm has yet to be realized
  Additional links for this entry:
  http://cogprints.org/324/0/thirdcontender.html
  http://cogprints.ecs.soton.ac.uk/archive/00000324/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/03/24/
  http://www.arts.uwaterloo.ca/~celiasmi/Papers/thirdcontender.html
  http://cogprints.ecs.soton.ac.uk/archive/00000324/00/thirdcontender.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:324
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a793914984~fulltext=713240930
  http://cogprints.org/324/1/thirdcontender.html
  
• Walter J. Freeman (1997). Nonlinear neurodynamics of intentionality.   (More)
  Additional links for this entry:
  http://sulcus.berkeley.edu/wjf/CL_DynamicsOfIntention.doc
  http://lecture.berkeley.edu/wjf/CL_DynamicsOfIntention.doc
  
• Robert M. French & Elizabeth Thomas (2001). The dynamical hypothesis in cognitive science: A review essay of Mind As Motion.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=596903
  http://citeseer.ist.psu.edu/french01dynamical.html
  http://www.u-bourgogne.fr/LEAD/people/french/mind_as_motion.pdf
  http://www.springerlink.com/content/content/xv2x648r418194mv/fulltext.pdf
  http://www.springerlink.com/content/xv2x648r418194mv/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=310383&PDF=1
  http://www.springerlink.com/index/XV2X648R418194MV.pdf
  
• Robert M. French & Elizabeth Thomas (1998). The dynamical hypothesis: One battle behind.   (Abstract & more)
  Abstract: What new implications does the dynamical hypothesis have for cognitive science? The short answer is: None. The _Behavior and Brain Sciences _target article, “The dynamical hypothesis in cognitive science” by Tim Van Gelder is basically an attack on traditional symbolic AI and differs very little from prior connectionist criticisms of it. For the past ten years, the connectionist community has been well aware of the necessity of using (and understanding) dynamically evolving, recurrent network models of cognition
  Additional links for this entry:
  http://citeseer.ist.psu.edu/french98dynamical.html
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=30498&jid=BBS&volumeId=21&issueId=05&aid=30497
  http://journals.cambridge.org/abstract_S0140525X98361737
  
• Gerard O’Brien (1998). Digital computers versus dynamical systems: A conflation of distinctions.   (Abstract & more)
  Abstract: The distinction at the heart of van Gelder’s target article is one between digital computers and dynamical systems. But this distinction conflates two more fundamental distinctions in cognitive science that should be keep apart. When this conflation is undone, it becomes apparent that the “computational hypothesis” (CH) is not as dominant in contemporary cognitive science as van Gelder contends; nor has the “dynamical hypothesis” (DH) been neglected
  Additional links for this entry:
  http://www.arts.adelaide.edu.au/humanities/philosophy/publications/Commentary_on_Van_Gelder_BBS.pdf
  http://www.arts.adelaide.edu.au/humanities/philosophy/publications/Commentary_on_Van_Gelder_BBS.pdf
  http://www.journals.cambridge.org/abstract_S0140525X98451732
  
• Erik Rietveld (2008). The Skillful Body as a Concernful System of Possible Actions: Phenomena and Neurodynamics.   (Abstract & more)
  Abstract: For Merleau-Ponty,consciousness in skillful coping is a matter of prereflective ‘I can’ and not explicit ‘I think that.’ The body unifies many domain-specific capacities. There exists a direct link between the perceived possibilities for action in the situation (‘affordances’) and the organism’s capacities. From Merleau-Ponty’s descriptions it is clear that in a flow of skillful actions, the leading ‘I can’ may change from moment to moment without explicit deliberation. How these transitions occur, however, is less clear. Given that Merleau-Ponty suggested that a better understanding of the self-organization of brain and behavior is important, I will re-read his descriptions of skillful coping in the light of recent ideas on neurodynamics. Affective processes play a crucial role in evaluating the motivational significance of objects and contribute to the individual’s prereflective responsiveness to relevant affordances.
• Stephen E. Robbins (2002). Semantics, experience and time.   (More)
  Additional links for this entry:
  http://linkinghub.elsevier.com/retrieve/pii/S1389041702000451
  http://www.ingentaconnect.com/content/els/13890417/2002/00000003/00000003/art00045
  
• Teed Rockwell (2005). Attractor spaces as modules: A semi-eliminative reduction of symbolic AI to dynamic systems theory.   (Abstract & more)
  Abstract: I propose a semi-eliminative reduction of Fodors concept of module to the concept of attractor basin which is used in Cognitive Dynamic Systems Theory (DST). I show how attractor basins perform the same explanatory function as modules in several DST based research program. Attractor basins in some organic dynamic systems have even been able to perform cognitive functions which are equivalent to the If/Then/Else loop in the computer language LISP. I suggest directions for future research programs which could find similar equivalencies between organic dynamic systems and other cognitive functions. This type of research could help us discover how (and/or if) it is possible to use Dynamic Systems Theory to more accurately model the cognitive functions that are now being modeled by subroutines in Symbolic AI computer models. If such a reduction of subroutines to basins of attraction is possible, it could free AI from the limitations that prompted Fodor to say that it was impossible to model certain higher level cognitive functions
  Additional links for this entry:
  http://www.springerlink.com/content/n6h75482x0qvt016/fulltext.pdf
  http://www.springerlink.com/index/N6H75482X0QVT016.pdf
  http://www.ingentaconnect.com/content/klu/mind/2005/00000015/00000001/00001344
  
• Teed Rockwell (online). Reply to Clark and Van gelder.   (Abstract & more)
  Abstract: Clark ends his appendix with a description of what he calls "dynamic computationalism", which he describes as an interesting hybrid between DST and GOFAI. My 'horseLISP" example could be described as an example of dynamic computationalism. It is clearly not as eliminativist as Van Gelder's computational governor example, for I am trying to come up with something like identities between computational entities and dynamic ones. Thus unlike other dynamicists, I am not doing what Clark calls "embracing a different vocabulary for the understanding and analysis of brain events". I think we probably can keep much of the computational vocabulary, although the meanings of many of its terms will probably shift as much as the meaning of 'atom' has shifted since Dalton's time. The label of "dynamic computationalism" is perhaps as good a description of my position as any, but I think I would mean something slightly different by it than Clark would. (For the following, please insert the mantra "of course, this is an empirical question" (OCTEQ) every paragraph or so.)
  Additional links for this entry:
  http://www.california.com/~mcmf/modreply.html
  http://users.california.com/~mcmf/modreply.html
  
• Teed Rockwell (1998). The modularity of dynamic systems.   (Abstract & more)
  Abstract: To some degree, Fodor's claim that Cognitive science divides the mind into modules tells us more about the minds doing the studying than the mind being studied. The knowledge game is played by analyzing an object of study into parts, and then figuring out how those parts are related to each other. This is the method regardless of whether the object being studied is a mind or a solar system. If a module is just another name for a part, then to say that the mind consists of modules is simply to say that it is comprehensible. Fodor comes close to acknowledging this in the following passage
  Additional links for this entry:
  http://www.california.com/~mcmf/mod.html
  http://users.california.com/~mcmf/mod.html
  
• W. Schonbein (2005). Cognition and the power of continuous dynamical systems.   (Abstract & more)
  Abstract: Traditional approaches to modeling cognitive systems are computational, based on utilizing the standard tools and concepts of the theory of computation. More recently, a number of philosophers have argued that cognition is too subtle or complex for these tools to handle. These philosophers propose an alternative based on dynamical systems theory. Proponents of this view characterize dynamical systems as (i) utilizing continuous rather than discrete mathematics, and, as a result, (ii) being computationally more powerful than traditional computational automata. Indeed, the logical possibility of such super-powerful systems has been demonstrated in the form of analog artificial neural networks. In this paper I consider three arguments against the nomological possibility of these automata. While the first two arguments fail, the third succeeds. In particular, the presence of noise reduces the computational power of analog networks to that of traditional computational automata, and noise is a pervasive feature of information processing in biological systems. Consequently, as an empirical thesis, the proposed dynamical alternative is under-motivated: What is required is an account of how continuously valued systems could be realized in physical systems despite the ubiquity of noise
  Additional links for this entry:
  http://www.springerlink.com/content/xtx321861761117r/fulltext.pdf
  http://www.springerlink.com/index/XTX321861761117R.pdf
  http://www.ingentaconnect.com/content/klu/mind/2005/00000015/00000001/00001345
  
• Aaron Sloman (1993). The mind as a control system.   (More)
  Additional links for this entry:
  http://citeseer.ist.psu.edu/sloman93mind.html
  http://www.cs.bham.ac.uk/research/cogaff/Aaron.Sloman_Mind.as.controlsystem.ps
  http://www.cs.bham.ac.uk/~axs/cog_affect/Aaron.Sloman_Mind.as.controlsystem.ps.gz
  
• John Symons (2001). Explanation, representation and the dynamical hypothesis.   (Abstract & more)
  Abstract:   This paper challenges arguments that systematic patterns of intelligent behavior license the claim that representations must play a role in the cognitive system analogous to that played by syntactical structures in a computer program. In place of traditional computational models, I argue that research inspired by Dynamical Systems theory can support an alternative view of representations. My suggestion is that we treat linguistic and representational structures as providing complex multi-dimensional targets for the development of individual brains. This approach acknowledges the indispensability of the intentional or representational idiom in psychological explanation without locating representations in the brains of intelligent agents
  Additional links for this entry:
  http://www.springerlink.com/content/u55n058l05q58r53/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=352659&PDF=1
  http://www.springerlink.com/index/U55N058L05Q58R53.pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000004/00352659
  
• Jan Treur (2005). States of change: Explaining dynamics by anticipatory state properties.   (Abstract & more)
  Abstract: In cognitive science, the dynamical systems theory (DST) has recently been advocated as an approach to cognitive modeling that is better suited to the dynamics of cognitive processes than the symbolic/computational approaches are. Often, the differences between DST and the symbolic/computational approach are emphasized. However, alternatively their commonalities can be analyzed and a unifying framework can be sought. In this paper, the possibility of such a unifying perspective on dynamics is analyzed. The analysis covers dynamics in cognitive disciplines, as well as in physics, mathematics and computer science. The unifying perspective warrants the development of integrated approaches covering both DST aspects and symbolic/computational aspects. The concept of a state-determined system, which is based on the assumption that properties of a given state fully determine the properties of future states, lies at the heart of DST. Taking this assumption as a premise, the explanatory problem of dynamics is analyzed in more detail. The analysis of four cases within different disciplines (cognitive science, physics, mathematics, computer science) shows how in history this perspective led to numerous often used concepts within them. In cognitive science, the concepts desire and intention were introduced, and in classical mechanics the concepts momentum, energy and force. Similarly, in mathematics a number of concepts have been developed to formalize the state-determined system assumption [e.g. derivatives (of different orders) of a function, Taylor approximations]. Furthermore, transition systems - a currently popular format for specification of dynamical systems within computer science - can also be interpreted from this perspective. One of the main contributions of the paper is that the case studies provide a unified view on the explanation of dynamics across the chosen disciplines. All approaches to dynamics analyzed in this paper share the state-determined system assumption and the (explicit or implicit) use of anticipatory state properties. Within cognitive science, realism is one of the problems identified for the symbolic/computational approach - i.e. how do internal states described by symbols relate to the real world in a natural manner. As DST is proposed as an alternative to the symbolic/computational approach, a natural question is whether, for DST, realism of the states can be better guaranteed. As a second main contribution, the paper provides an evaluation of DST compared to the symbolic/computational approach, which shows that, in this respect (i.e. for the realism problem), DST does not provide a better solution than the other approaches. This shows that DST and the symbolic/computational approach not only have the state-determined system assumption and the use of anticipatory state properties in common, but also the realism problem
  Additional links for this entry:
  http://www.few.vu.nl/~wai/Papers/PP04soc.pdf
  http://www.cs.vu.nl/~wai/Papers/PP04socprel.pdf
  http://www.phil.uu.nl/preprints/ckipreprints/PREPRINTS/preprint051.pdf
  http://www.informaworld.com/smpp/./ftinterface~content=a723861123~fulltext=713240930
  http://taylorandfrancis.metapress.com/index/HU51317GJ73622Q1.pdf
  http://www.informaworld.com/index/723861123.pdf
  http://www.ingentaconnect.com/content/routledg/cphp/2005/00000018/00000004/art00003
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a723861123~fulltext=713240930
  
• Tim van Gelder (1998). Disentangling dynamics, computation, and cognition.   (Abstract & more)
  Abstract: The nature of the dynamical hypothesis in cognitive science (the DH) is further clarified in responding to various criticisms and objections raised in commentaries. Major topics addressed include the definitions of “dynamical system” and “digital computer;” the DH as Law of Qualitative Structure; the DH as an ontological claim; the multiple-realizability of dynamical models; the level at which the DH is pitched; the nature of dynamics; the role of representations in dynamical cognitive science; the falsifiability of the DH; the extent to which the DH is open; the role of temporal and implementation considerations; and the novelty or importance of the DH. The basic formulation and defense of the DH in the target article survives intact, though some refinements are recommended
  Additional links for this entry:
  http://www.philosophy.unimelb.edu.au/tgelder/papers/DHResp.pdf
  http://www.phil.canterbury.ac.nz/tom_bestor/e-texts/van Gelder - Dynamtical Hypothesis in Cog Sci Authors Responses.pdf
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=30876&jid=BBS&volumeId=21&issueId=05&aid=30875
  http://www.journals.cambridge.org/abstract_S0140525X98521735
  http://journals.cambridge.org/abstract_S0140525X98521735
  
• Tim van Gelder (1999). Defending the dynamic hypothesis.   (Abstract & more)
  Abstract: Cognitive science has always been dominated by the idea that cognition is _computational _in a rather strong and clear sense. Within the mainstream approach, cognitive agents are taken to be what are variously known as _physical symbol_ _systems, digital computers_, _syntactic engines_, or_ symbol manipulators_. Cognitive operations are taken to consist in the shuffling of symbol tokens according to strict rules (programs). Models of cognition are themselves digital computers, implemented on general purpose electronic machines. The basic mathematical framework for understanding cognition is the theory of discrete computation, and the core theoretical tools for developing and understanding models of cognition are those of computer science
  Additional links for this entry:
  http://citeseer.ist.psu.edu/292698.html
  http://citeseer.ist.psu.edu/vangelder98defending.html
  http://depts.washington.edu/edtech/VanGelder_revisiting.pdf
  http://books.google.com/books?hl=en&lr=&id=BlRAVPwgsMwC&oi=fnd&pg=PA13&ots=weyOd5KZHZ&sig=0xdkpD2AVxZ3VXmohG8YuqJieNg
  
• Tim van Gelder (1999). Revisiting the dynamic hypothesis.   (Abstract & more)
  Abstract: “There is a familiar trio of reactions by scientists to a purportedly radical hypothesis: (a) “You must be our of your mind!”, (b) “What else is new? Everybody knows _that_!”, and, later—if the hypothesis is still standing—(c) “Hmm. You _might _be on to something!” ((Dennett, 1995) p. 283)
  Additional links for this entry:
  http://depts.washington.edu/edtech/VanGelder_revisiting.pdf
  
• Tim van Gelder (1998). The dynamical hypothesis in cognitive science.   (Abstract & more)
  Abstract: The dynamical hypothesis is the claim that cognitive agents are dynamical systems. It stands opposed to the dominant computational hypothesis, the claim that cognitive agents are digital computers. This target article articulates the dynamical hypothesis and defends it as an open empirical alternative to the computational hypothesis. Carrying out these objectives requires extensive clarification of the conceptual terrain, with particular focus on the relation of dynamical systems to computers
  Additional links for this entry:
  http://www.irit.fr/SIGCHI/old/docs/debat/DH.pdf
  http://bbsonline.cup.cam.ac.uk/Preprints/OldArchive/bbs.vangelder.html
  http://www.bbsonline.org/documents/a/00/00/04/68/bbs00000468-00/bbs.vangelder.html
  http://journals.cambridge.org/action/displayFulltext?type=1&fid=30004&jid=BBS&volumeId=21&issueId=05&aid=30003
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=10097022&dopt=Citation
  http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=10097022&cmd=showdetailview&indexed=google
  http://www.journals.cambridge.org/abstract_S0140525X98001733
  http://journals.cambridge.org/abstract_S0140525X98001733
  http://journals.cambridge.org/article_S0140525X98301739
  http://journals.cambridge.org/article_S0140525X98331738
  
• Tim van Gelder (1995). What might cognition be if not computation?   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=FEnPpGB6HE0C&oi=fnd&pg=PT158&ots=iLWO_4GjfB&sig=_-hLgEKPrvzRGqiHU3WB-ByPxG4
  http://books.google.com/books?hl=en&lr=&id=FEnPpGB6HE0C&oi=fnd&pg=PT158&ots=iK3S02IheD&sig=9bvnduaEm9LGyoZvLb9vrkbzLwc
  http://books.google.com/books?hl=en&lr=&id=FEnPpGB6HE0C&oi=fnd&pg=PT158&ots=iLWOW7Jo9y&sig=nbynhfg_nmoW_oos5qyFfZKaq_w
  http://www.jstor.org/sici?sici=0022-362X(199507)92:7<345:WMCBIN>2.0.CO;2-F
  http://www.jstor.org/stable/pdfplus/2941061.pdf
  
• Daniel A. Weiskopf (2004). The place of time in cognition.   (Abstract & more)
  Abstract: models of cognition are essentially incomplete because they fail to capture the temporal properties of mental processing. I present two possible interpretations of the dynamicists' argument from time and show that neither one is successful. The disagreement between dynamicists and symbolic theorists rests not on temporal considerations per se, but on differences over the multiple realizability of cognitive states and the proper explanatory goals of psychology. The negative arguments of dynamicists against symbolic models fail, and it is doubtful whether pursuing dynamicists' explanatory goals will lead to a robust psychological theory. Introduction Elements of the symbolic theory Elements of dynamical systems theory The argument from time 4.1 First interpretation of the argument from time 4.2 Second interpretation of the argument from time Limits of dynamical systems theory
  Additional links for this entry:
  http://bjps.oxfordjournals.org/cgi/content/abstract/55/1/87
  http://bjps.oupjournals.org/cgi/content/abstract/55/1/87
  http://bjps.oxfordjournals.org/cgi/reprint/55/1/87
  http://www.ingentaconnect.com/searching/Expand?pub=infobike://oup/phisci/2004/00000055/00000001/art00087&unc=
  http://www.ingentaconnect.com/content/oup/phisci/2004/00000055/00000001/art00087
  
• Markus Werning (2005). The temporal dimension of thought: Cortical foundations of predicative representation.   (Abstract & more)
  Abstract: The paper argues that cognitive states of biological systems are inherently temporal. Three adequacy conditions for neuronal models of representation are vindicated: the compositionality of meaning, the compositionality of content, and the co-variation with content. Classicist and connectionist approaches are discussed and rejected. Based on recent neurobiological data, oscillatory networks are introduced as a third alternative. A mathematical description in a Hilbert space framework is developed. The states of this structure can be regarded as conceptual representations satisfying the three conditions
  Additional links for this entry:
  http://service.phil-fak.uni-duesseldorf.de/ezpublish/index.php/filemanager/download/282/syntcog12.pdf
  http://www.springerlink.com/content/r522585100067t0p/fulltext.pdf
  http://www.springerlink.com/index/R522585100067T0P.pdf
  http://www.ingentaconnect.com/content/klu/synt/2005/00000146/F0020001/00009089
  

The Nature of AI

• A. Bundy (1990). What kind of field is AI?   (More)
• John McCarthy (online). What is artificial intelligence?   (More)
  Additional links for this entry:
  http://www-formal.stanford.edu/jmc/whatisai.pdf
  http://airobo.persiangig.com/document/whatisai.pdf
  http://www.formal.stanford.edu/jmc/whatisai/whatisai.html
  http://www.kurzweilai.net/articles/art0088.html?printable=1
  http://www.fredbf.com/disciplinas/unibratec/dsi/whatisai.pdf
  http://www.inf.furb.br/~jomi/ia/introducao/mccarthy-whatisai.ps
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:412
  
• Marvin L. Minsky (online). From pain to suffering.   (Abstract & more)
  Abstract: “Great pain urges all animals, and has urged them during endless generations, to make the most violent and diversified efforts to escape from the cause of suffering. Even when a limb or other separate part of the body is hurt, we often see a tendency to shake it, as if to shake off the cause, though this may obviously be impossible.” —Charles Darwin[1]
• H. Nakashima (1999). AI as complex information processing.   (Abstract & more)
  Abstract:   In this article, I present a software architecture for intelligent agents. The essence of AI is complex information processing. It is impossible, in principle, to process complex information as a whole. We need some partial processing strategy that is still somehow connected to the whole. We also need flexible processing that can adapt to changes in the environment. One of the candidates for both of these is situated reasoning, which makes use of the fact that an agent is in a situation, so it only processes some of the information – the part that is relevant to that situation. The combination of situated reasoning and context reflection leads to the idea of organic programming, which introduces a new building block of programs called a cell. Cells contain situated programs and the combination of cells is controlled by those programs
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=467235CI
  http://www.springerlink.com/content/ju27445787217334/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=187742&PDF=1
  http://www.springerlink.com/index/JU27445787217334.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000009/00000001/00187742
  
• Aaron Sloman (2002). The irrelevance of Turing machines to AI.   (More)
  Additional links for this entry:
  http://citeseer.ist.psu.edu/sloman02irrelevance.html
  http://www.cs.bham.ac.uk/research/cogaff/sloman.turing.irrelevant.ps
  http://www.cs.bham.ac.uk/research/cogaff/sloman.turing.irrelevant.pdf
  http://www-lehre.informatik.uni-osnabrueck.de/~yzhao/tcn/sloman.turing.pdf
  http://www.cs.bham.ac.uk/research/projects/cogaff/sloman.turing.irrelevant.pdf
  
• Kenneth J. Sufka & Thomas W. Polger (2005). Closing the gap on pain.   (Abstract & more)
  Abstract: A widely accepted theory holds that emotional experiences occur mainly in a part of the human brain called the amygdala. A different theory asserts that color sensation is located in a small subpart of the visual cortex called V4. If these theories are correct, or even approximately correct, then they are remarkable advances toward a scientific explanation of human conscious experience. Yet even understanding the claims of such theories—much less evaluating them—raises some puzzles. Conscious experience does not present itself as a brain process. Indeed experience seems entirely unlike neural activity. For example, to some people it seems that an exact physical duplicate of you could have different sensations than you do, or could have no sensations at all. If so, then how is it even possible that sensations could turn out to be brain processes?
  Additional links for this entry:
  http://oz.uc.edu/~polgertw/PolgerSufka-ClosingGapPain.pdf
  http://homepages.uc.edu/~polgertw/PolgerSufka-ClosingGapPain.pdf
  

The Frame Problem

• Andy Clark (2002). Global abductive inference and authoritative sources, or, how search engines can save cognitive science.   (Abstract & more)
  Abstract: Kleinberg (1999) describes a novel procedure for efficient search in a dense hyper-linked environment, such as the world wide web. The procedure exploits information implicit in the links between pages so as to identify patterns of connectivity indicative of “authorative sources”. At a more general level, the trick is to use this second-order link-structure information to rapidly and cheaply identify the knowledge- structures most likely to be relevant given a specific input. I shall argue that Kleinberg’s procedure is suggestive of a new, viable, and neuroscientifically plausible solution to at least (one incarnation of) the so-called “Frame Problem” in cognitive science viz the problem of explaining global abductive inference. More accurately, I shall argue that
  Additional links for this entry:
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/GlobalReason.pdf
  
• James H. Fetzer (1990). The frame problem: Artificial intelligence meets David Hume.   (More)
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  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2737634CI
  
• W. F. G. Haselager & J. F. H. Van Rappard (1998). Connectionism, systematicity, and the frame problem.   (Abstract & more)
  Abstract:   This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts can be raised about the potential of distributed representations to allow large amounts of complexly structured information to be adequately encoded and processed. It is questionable whether connectionist models that are claimed to effectively represent structured information can be scaled up to a realistic extent. We conclude that the frame problem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=371129CI
  http://www.springerlink.com/content/um0x474450532866/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=150466&PDF=1
  http://www.springerlink.com/index/UM0X474450532866.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000008/00000002/00150466
  
• Scott Hendricks (2006). The frame problem and theories of belief.   (Abstract & more)
  Abstract: The frame problem is the problem of how we selectively apply relevant knowledge to particular situations in order to generate practical solutions. Some philosophers have thought that the frame problem can be used to rule out, or argue in favor of, a particular theory of belief states. But this is a mistake. Sentential theories of belief are no better or worse off with respect to the frame problem than are alternative theories of belief, most notably, the “map” theory of belief
  Additional links for this entry:
  http://www.springerlink.com/content/p846610503j81603/fulltext.pdf
  http://www.springerlink.com/index/P846610503J81603.pdf
  http://www.ingentaconnect.com/content/klu/phil/2006/00000129/00000002/00001644
  
• Kevin B. Korb (1998). The frame problem: An AI fairy tale.   (Abstract & more)
  Abstract:   I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=381462CI
  http://www.springerlink.com/content/q30166435n81x313/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=168538&PDF=1
  http://www.springerlink.com/index/Q30166435N81X313.pdf
  http://www.ingentaconnect.com/content/klu/mind/1998/00000008/00000003/00168538
  
• Eric Lormand (1998). The frame problem.   (Abstract & more)
  Abstract: From its humble origins labeling a technical annoyance for a particular AI formalism, the term "frame problem" has grown to cover issues confronting broader research programs in AI. In philosophy, the term has come to encompass allegedly fundamental, but merely superficially related, objections to computational models of mind in AI and beyond
• Eric Lormand (1994). The holorobophobe's dilemma.   (Abstract & more)
  Abstract: Much research in AI (and cognitive science, more broadly) proceeds on the assumption that there is a difference between being well-informed and being smart. Being well-informed has to do, roughly, with the content of one’s representations--with their truth and the range of subjects they cover. Being smart, on the other hand, has to do with one’s ability to process these representations and with packaging them in a form that allows them to be processed efficiently. The main theoretical concern of artificial intelligence research is to solve "process-and-form" problems: problems with finding processes and representational formats that enable us to understand how a computer could be smart
  Additional links for this entry:
  http://www-personal.umich.edu/~lormand/phil/cogsci/holorobophobe.htm
  http://books.google.com/books?hl=en&lr=&id=FubMrc6I4DQC&oi=fnd&pg=PA61&ots=8FqLfSQJvI&sig=hkiEKXzQKsG9Gm3nxwiIFbbaoPw
  
• John McCarthy & Patrick Hayes (1969). Some philosophical problems from the standpoint of artificial intelligence.   (More)
  Additional links for this entry:
  http://www-formal.stanford.edu/jmc/mcchay69.pdf
  http://www.inf.ufsc.br/~mauro/ine6102/leituras/mcchay69.pdf
  http://www.dfki.uni-sb.de/imedia/lidos/bibtex/GINSBERG_a5141-152.html
  http://www.ida.liu.se/ext/brs/A/McCarthy,John/J.McCarthy69A/descrip.adl
  http://www.spatial.maine.edu/~worboys/processes/mccarthy hayes situation calculus.pdf
  
• Zenon Pylyshyn (1996). The frame problem blues. Once more, with feeling.   (Abstract & more)
  Abstract: For many of the authors in this volume, this is the second attempt to explore what McCarthy and Hayes (1969) first called the “Frame Problem”. Since the first compendium (Pylyshyn, 1987), nicely summarized here by Ronald Loui, there have been several conferences and books on the topic. Their goals range from providing a clarification of the problem by breaking it down into subproblems (and sometimes declaring the hard subproblems to not be the_ real_ Frame Problem), to providing formal “solutions” to certain aspects of the problem. But more often the message has been that the problem is not solvable except in a piecemeal way in special circumstances by some sort of heuristic approximations. It has sometimes also been said that solving the Frame Problem is not only an unachievable goal, but it is also an unnecessary one since_ humans_ do not solve it either; we simply get along as best we can and deal with the problem of planning in ways that, to use Dennett’s phrase, is “good enough for government work”
  Additional links for this entry:
  http://ruccs.rutgers.edu/ftp/pub/papers/frame.pdf
  http://portal.acm.org/citation.cfm?id=279932.279934
  
• Murray Shanahan & Bernard J. Baars (2005). Applying global workspace theory to the frame problem.   (More)
  Additional links for this entry:
  http://eric.ed.gov/ERICWebPortal/recordDetail?accno=EJ724310
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=16307957&dopt=Citation
  http://linkinghub.elsevier.com/retrieve/pii/S0010027704002288
  
• Murray Shanahan (online). The frame problem.   (More)
• Dan Sperber & Deirdre Wilson (1996). Fodor's frame problem and relevance theory (reply to chiappe & kukla).   (Abstract & more)
  Abstract: Chiappe and Kukla argue that relevance theory fails to solve the frame problem as defined by Fodor. They are right. They are wrong, however, to take Fodor’s frame problem too seriously. Fodor’s concerns, on the other hand, even though they are wrongly framed, are worth addressing. We argue that Relevance thoery helps address them
  Additional links for this entry:
  http://cogprints.org/2004/1/frame.htm
  http://cogprints.org/2029/1/frame.htm
  http://cogprints.org/2004/0/frame.htm
  
• Mark Sprevak, The frame problem and the treatment of prediction.   (Abstract & more)
  Abstract: The frame problem is a problem in artificial intelligence that a number of philosophers have claimed has philosophical relevance. The structure of this paper is as follows: (1) An account of the frame problem is given; (2) The frame problem is distinguished from related problems; (3) The main strategies for dealing with the frame problem are outlined; (4) A difference between commonsense reasoning and prediction using a scientific theory is argued for; (5) Some implications for the..

AI Methodology

• Mark H. Bickhard (2000). Motivation and Emotion: An Interactive Process Model.   (Abstract & more)
  Abstract: In this chapter, I outline dynamic models of motivation and emotion. These turn out not to be autonomous subsystems, but, instead, are deeply integrated in the basic interactive dynamic character of living systems. Motivation is a crucial aspect of particular kinds of interactive systems -- systems for which representation is a sister aspect. Emotion is a special kind of partially reflective interaction process, and yields its own emergent motivational aspects. In addition, the overall model accounts for some of the crucial properties of consciousness
  Additional links for this entry:
  http://www.lehigh.edu/~mhb0/motemotion.html
  http://www.lehigh.edu/~mhb0/MotandEmotion.pdf
  
• L. Birnbaum (1991). Rigor mortis: A response to Nilsson's 'logic and artificial intelligence'.   (More)
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2460287CI
  
• David J. Chalmers, Robert M. French & Douglas R. Hofstadter (1992). High-level perception, representation, and analogy:A critique of artificial intelligence methodology.   (Abstract & more)
  Abstract: High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context
  Additional links for this entry:
  http://citeseer.ist.psu.edu/49715.html
  http://portal.acm.org/citation.cfm?id=175346.175347
  http://www.u.arizona.edu/~chalmers/papers/highlevel.pdf
  http://www.nbu.bg/cogs/personal/kokinov/COG501/hightlevel.pdf
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2767459CI
  http://www.informaworld.com/index/778787585.pdf
  
• Giovanna Colombetti (2007). Enactive appraisal.   (Abstract & more)
  Abstract: Emotion theorists tend to separate “arousal” and other bodily events such as “actions” from the evaluative component of emotion known as “appraisal.” This separation, I argue, implies phenomenologically implausible accounts of emotion elicitation and personhood. As an alternative, I attempt a reconceptualization of the notion of appraisal within the so-called “enactive approach.” I argue that appraisal is constituted by arousal and action, and I show how this view relates to an embodied and affective notion of personhood
  Additional links for this entry:
  http://polorovereto.unitn.it/~colombetti/docs/GC_EnactiveAppraisal06.pdf
  http://polorovereto.unitn.it/~colombetti/docs/GC_EnactiveAppraisal06.pdf
  http://www.springerlink.com/content/y7543g7p14826726/fulltext.pdf
  http://www.springerlink.com/index/Y7543G7P14826726.pdf
  
• Giovanna Colombetti & Evan Thompson (forthcoming). The feeling body: Towards an enactive approach to emotion.   (Abstract & more)
  Abstract: For many years emotion theory has been characterized by a dichotomy between the head and the body. In the golden years of cognitivism, during the nineteen-sixties and seventies, emotion theory focused on the cognitive antecedents of emotion, the so-called “appraisal processes.” Bodily events were seen largely as byproducts of cognition, and as too unspecific to contribute to the variety of emotion experience. Cognition was conceptualized as an abstract, intellectual, “heady” process separate from bodily events. Although current emotion theory has moved beyond this disembodied stance by conceiving of emotions as involving both cognitive processes (perception, attention, and evaluation) and bodily events (arousal, behavior, and facial expressions), the legacy of cognitivism persists in the tendency to treat cognitive and bodily events as separate constituents of emotion. Thus the cognitive aspects of emotion are supposedly distinct and separate from the bodily ones. This separation indicates that cognitivism’s disembodied conception of cognition continues to shape the way emotion theorists conceptualize emotion
  Additional links for this entry:
  http://individual.utoronto.ca/evant/FeelingBody.pdf
  http://people.exeter.ac.uk/gc243/index/GC&ET_FeelingBody.pdf
  http://polorovereto.unitn.it/~colombetti/docs/GC&ET_FeelingBody.pdf
  
• Eric Dietrich (1994). AI and the tyranny of Galen, or why evolutionary psychology and cognitive ethology are important to artificial intelligence.   (Abstract & more)
  Abstract: Concern over the nature of AI is, for the tastes many AI scientists, probably overdone. In this they are like all other scientists. Working scientists worry about experiments, data, and theories, not foundational issues such as what their work is really about or whether their discipline is methodologically healthy. However, most scientists aren’t in a field that is approximately fifty years old. Even relatively new fields such as nonlinear dynamics or branches of biochemistry are in fact advances in older established sciences and are therefore much more settled. Of course, by stretching things, AI can be said to have a history reaching back t o Charles Babbage, and possibly back beyond that to Leibnitz. However, all of that is best viewed as prelude. AI’s history is punctuated with the invention of the computer (and, if one wants t o stretch our history back to the 1930s, the development of the notion of computation by Turing, Church, and others). Hence, AI really began (or began in earnest) sometime in the late 1940s or early 1950s (some mark the conference a t Dartmouth in the summer of 1957 as the moment of our birth). And since those years we simply have not had time to settle into a routine science attacking reasonably well understood questions (for example, many of the questions some of us regard as supreme are regarded by others as inconsequential or mere excursions)
  Additional links for this entry:
  http://www.informaworld.com/index/777915170.pdf
  
• Hubert L. Dreyfus (2007). Why Heideggerian ai failed and how fixing it would require making it more Heideggerian.   (More)
  Additional links for this entry:
  http://www.informaworld.com/smpp/./ftinterface~content=a777583844~fulltext=713240930
  http://www.informaworld.com/index/777583844.pdf
  http://www.informaworld.com/smpp/./ftinterface~db=all~content=a777583844~fulltext=713240930
  
• Jon Elster (1996). Rationality and the emotions.   (Abstract & more)
  Abstract: In an earlier paper (Elster, 1989 a), I discussed the relation between rationality and social norms. Although I did mention the role of the emotions in sustaining social norms, I did not focus explicitly on the relation between rationality and the emotions. That relation is the main topic of the present paper, with social norms in a subsidiary part
  Additional links for this entry:
  http://ideas.repec.org/a/ecj/econjl/v106y1996i438p1386-97.html
  http://culturalheritage.ceistorvergata.it/virtual_library/Art. - Rationality and emotions - J. ELSTER.pdf
  http://orton.catie.ac.cr/cgi-bin/wxis.exe/?IsisScript=IDEA.xis&method=post&formato=2&cantidad=1&expresion=mfn=001739
  http://links.jstor.org/sici?sici=0013-0133(199609)106:438<1386:RATE>2.0.CO;2-T
  
• Paul E. Griffiths & Andrea Scarantino (2005). Emotions in the Wild: The Situated Perspective on Emotion.   (Abstract & more)
  Abstract: Paul E Griffiths Biohumanities Project University of Queensland St Lucia 4072 Australia paul.griffiths@uq.edu.au
  Additional links for this entry:
  http://philsci-archive.pitt.edu/archive/00002448/
  http://www.uq.edu.au/biohumanities/webpdfs/EmotionsintheWild.PDF
  http://www.uq.edu.au/biohumanities/webpdfs/EmotionsintheWild.PDF
  http://paul.representinggenes.org/webpdfs/Griff.Scara.IP.EmotionsWild.pdf
  http://philsci-archive.pitt.edu/archive/00002448/01/Emotions_in_the_Wild.PDF
  
• Robert F. Hadley (1991). The many uses of 'belief' in AI.   (Abstract & more)
  Abstract:   Within AI and the cognitively related disciplines, there exist a multiplicity of uses of belief. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called belief. In this paper I distinguish six distinct ways in which belief is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special AI applications. In other cases, however, genuine differences exist about the nature of what we pre-theoretically call belief. To an extent the multiplicity of opinions about, and uses of belief, echoes the discrepant motivations of AI researchers. The relevance of this discussion for cognitive scientists and philosophers arises from the fact that (a) many regard theoretical research within AI as a branch of cognitive science, and (b) even if theoretical AI is not cognitive science, trends within AI influence theories developed within cognitive science. It should be beneficial, therefore, to unravel the distinct uses and motivations surrounding belief, in order to discover which usages merely reflect differing pragmatic concerns, and which usages genuinely reflect divergent views about reality
  Additional links for this entry:
  http://www.springerlink.com/content/qm781236u5642248/fulltext.pdf
  http://www.springerlink.com/index/QM781236U5642248.pdf
  
• David Kirsh (1991). Foundations of AI: The big issues.   (Abstract & more)
  Abstract: The objective of research in the foundations of Al is to explore such basic questions as: What is a theory in Al? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core Al is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding problem. (3) Cognition is nicely described in propositional terms. (4) We can study cognition separately from learning. (5) There is a single architecture underlying virtually all cognition. I explain what each of these implies and present arguments from both outside and inside Al why each has been seen as right or wrong.
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2460285CI
  
• Willem A. Labuschagne & Johannes Heidema (2005). Natural and artificial cognition: On the proper place of reason.   (More)
  Additional links for this entry:
  http://www.ajol.info/viewarticle.php?jid=211&id=24184
  http://www.cs.otago.ac.nz/homepages/willem/publications/SAJP paper.rtf
  
• David Marr (1977). Artificial intelligence: A personal view.   (More)
  Additional links for this entry:
  https://dspace.mit.edu/handle/1721.1/5776?mode=simple
  http://www.inf.ed.ac.uk/teaching/modules/irm/marr.ps.gz
  http://www.inf.ed.ac.uk/teaching/courses/irm/marr.ps.gz
  http://books.google.com/books?hl=en&lr=&id=G0lJqbM15gsC&oi=fnd&pg=PA37&ots=fpIHwpf-A3&sig=TFXshtTLSQeKKpJCvrg1gvOCbDg
  
• Drew McDermott (1981). Artificial intelligence meets natural stupidity.   (More)
• Neil Nilsson (1991). Logic and artificial intelligence.   (More)
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2460286CI
  http://books.google.com/books?hl=en&lr=&id=gHmayvQUS-0C&oi=fnd&pg=PA31&ots=Ncnpqvy7wH&sig=6iT-5H55h1UY0sB27skFDMVYdDc
  
• Stephen Petersen (2004). Functions, creatures, learning, emotion.   (Abstract & more)
  Abstract: I propose a conceptual framework for emotions according to which they are best understood as the feedback mechanism a creature possesses in virtue of its function to learn. More specifically, emotions can be neatly modeled as a measure of harmony in a certain kind of constraint satisfaction problem. This measure can be used as error for weight adjustment (learning) in an unsupervised connectionist network.
• J. Sticklen (1989). Problem-solving architectures at the knowledge level.   (More)
  Additional links for this entry:
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=2213245CI
  http://www.informaworld.com/index/777585783.pdf
  
• Matthew Stone, Agents in the real world.   (Abstract & more)
  Abstract: The mid-twentieth century saw the introduction of a new general model of processes, COMPUTATION, with the work of scientists such as Turing, Chomsky, Newell and Simon.1 This model so revolutionized the intellectual world that the dominant scientific programs of the day—spearheaded by such eminent scientists as Hilbert, Bloomfield and Skinner—are today remembered as much for the way computation exposed their stark limitations as for their positive contributions.2 Ever since, the field of Artificial Intelligence (AI) has defined itself as the subfield of computer science dedicated to the understanding of intelligent entities as computational processes. Now, drawing on fifty years of results of increasing breadth and applicability, we can also characterize AI research as a concrete practice: an ENGINEER-

Robotics

• Anthony F. Beavers, Between angels and animals: The question of robot ethics, or is Kantian moral agency desirable?   (Abstract & more)
  Abstract: In this paper, I examine a variety of agents that appear in Kantian ethics in order to determine which would be necessary to make a robot a genuine moral agent. However, building such an agent would require that we structure into a robot’s behavioral repertoire the possibility for immoral behavior, for only then can the moral law, according to Kant, manifest itself as an ought, a prerequisite for being able to hold an agent morally accountable for its actions. Since building a moral robot requires the possibility of immoral behavior, I go on to argue that we cannot morally want robots to be genuine moral agents, but only beings that simulate moral behavior. Finally, I raise but do not answer the question that if morality requires us to want robots that are not genuine moral agents, why should we want something different in the case of human beings
• Rodney A. Brooks & Lynn Andrea Stein (1994). Building brains for bodies.   (Abstract & more)
  Abstract: We describe a project to capitalize on newly available levels of computational resources in order to understand human cognition. We are building an integrated physical system including vision, sound input and output, and dextrous manipulation, all controlled by a continuously operating large scale parallel MIMD computer. The resulting system will learn to "think" by building on its bodily experiences to accomplish progressively more abstract tasks. Past experience suggests that in attempting to build such an integrated system we will have to fundamentally change the way artificial intelligence, cognitive science, linguistics, and philosophy think about the organization of intelligence. We expect to be able to better reconcile the theories that will be developed with current work in neuroscience
  Additional links for this entry:
  https://dspace.mit.edu/handle/1721.1/5948
  http://www.ai.mit.edu/people/brooks/papers/brains.pdf
  http://people.csail.mit.edu/people/brooks/papers/brains.pdf
  http://www.aisland.org/cki20/media/handouts/frederic_zolnet.doc
  http://wexler.free.fr/library/files/brooks (1994) building brains for bodies.pdf
  http://www.ece.pdx.edu/~mperkows/ML_LAB/Giant_Hexapod/building-braind-brooks.pdf
  http://stinet.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA270531
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=200163015253CE&recid=A9432374AH
  http://www.springerlink.com/index/QW4834246H1K5537.pdf
  
• Rodney Brooks (1991). Challenges for Complete Creature Architectures.   (Abstract & more)
  Abstract: boundaries. It is impossible to do good science without having an appreciation for the problems and concepts in the other levels of abstraction (at least in the direction from biology towards physics), but there are whole sets of tools, methods of analysis, theories and explanations within each discipline which do not cross those boundaries
  Additional links for this entry:
  http://citeseer.ist.psu.edu/brooks90challenges.html
  http://people.csail.mit.edu/brooks/papers/challenges.pdf
  http://www.cs.helsinki.fi/u/jphuttun/histo/source/challenges.pdf
  http://www.valentiniweb.com/Piermo/robotica/doc/Brooks/challenges.pdf
  http://hebb.cis.uoguelph.ca/~dastacey/Robotics/Brooks/Brookschallenges.ps
  http://www.ee.pdx.edu/~mperkows/ML_LAB/Giant_Hexapod/brooks-challenges.pdf
  http://www.ece.pdx.edu/~mperkows/ML_LAB/Giant_Hexapod/brooks-challenges.pdf
  
• Rodney A. Brooks, Cynthia Breazeal, Matthew Marjanovic, Brian Scassellati & Matthew Williamson (1999). The cog project: Building a humanoid robot.   (Abstract & more)
  Abstract: To explore issues of developmental structure, physical em- bodiment, integration of multiple sensory and motor systems, and social interaction, we have constructed an upper-torso humanoid robot called Cog. The robot has twenty-one degrees of freedom and a variety of sen- sory systems, including visual, auditory, vestibular, kinesthetic, and tac- tile senses. This chapter gives a background on the methodology that we have used in our investigations, highlights the research issues that have been raised during this project, and provides a summary of both the current state of the project and our long-term goals. We report on a variety of implemented visual-motor routines (smooth-pursuit track- ing, saccades, binocular vergence, and vestibular-ocular and opto-kinetic re?exes), orientation behaviors, motor control techniques, and social be- haviors (pointing to a visual target, recognizing joint attention through face and eye ?nding, imitation of head nods, and regulating interaction through expressive feedback). We further outline a number of areas for future research that will be necessary to build a complete embodied sys- tem
  Additional links for this entry:
  http://www.cs.yale.edu/homes/scaz/papers/springer-final-cog.pdf
  http://groups.csail.mit.edu/lbr/hrg/1998/springer-final-cog.pdf
  http://cs-www.cs.yale.edu/homes/scaz/papers/springer-final-cog.pdf
  http://www.cc.gatech.edu/classes/AY2003/cs4803b_spring/readings/brooks99cog.pdf
  http://www.ee.pdx.edu/~mperkows/CLASS_ROBOTICS/FEBR26-2004/Humanoids/cog-project-brooks99cog.pdf
  https://longway.dell11.com/03http-14777.cq.zq1.Wx6/jevrWg1G/NAe/wei/fooJ--/springer-final-cog.pdf
  http://books.google.com/books?hl=en&lr=&id=B0JrZ7nJGWAC&oi=fnd&pg=PA52&ots=93oWiQzUcJ&sig=e5kkWmVDS2-6Fa5ty-n0_WWIjfE
  http://www.springerlink.com/index/q0ly9uyvwaddmdxj.pdf
  http://www.springerlink.com/index/Q0LY9UYVWADDMDXJ.pdf
  
• Joanna J. Bryson (2006). The attentional spotlight (dennett and the cog project).   (More)
  Additional links for this entry:
  http://www.springerlink.com/index/B32K3HT479101W80.pdf
  
• Alain Cardon (2006). Artificial consciousness, artificial emotions, and autonomous robots.   (More)
  Additional links for this entry:
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=17016730&dopt=Citation
  http://www.springerlink.com/index/T03M83873362716L.pdf
  http://www.ingentaconnect.com/content/klu/10339/2006/00000007/00000004/00000154
  
• William Clancey (1995). How situated cognition is different from situated robotics.   (More)
• Andy Clark & Rick Grush (1999). Towards a cognitive robotics.   (Abstract & more)
  Abstract: There is a definite challenge in the air regarding the pivotal notion of internal representation. This challenge is explicit in, e.g., van Gelder, 1995; Beer, 1995; Thelen & Smith, 1994; Wheeler, 1994; and elsewhere. We think it is a challenge that can be met and that (importantly) can be met by arguing from within a general framework that accepts many of the basic premises of the work (in new robotics and in dynamical systems theory) that motivates such scepticism in the first place. Our strategy will be as follows. We begin (Section 1) by offering an account (an example and something close to a definition) of what we shall term Minimal Robust Representationalism (MRR). Sections 2 & 3 address some likely worries and questions about this notion. We end (Section 4) by making explicit the conditions under which, on our account, a science (e.g., robot- ics) may claim to be addressing cognitive phenomena
  Additional links for this entry:
  http://www.isab.org/journal/adap7_1.php
  http://portal.acm.org/citation.cfm?id=312918
  http://mind.ucsd.edu/papers/cogrob/cogrob.pdf
  https://www.era.lib.ed.ac.uk/handle/1842/1297
  http://mind.ucsd.edu/papers/cogrob/cogrob.pdf
  http://www.denizyuret.com/ref/clark_andy/cogrob.ps
  http://www.cogs.indiana.edu/andy/tacrfinalw-Grush.pdf
  http://www.cogs.indiana.edu/andy/tacrfinalw-Grush.pdf
  http://mind.ucsd.edu/papers/cogrob/cogrobhtml/cogrob-text.html
  http://mind.ucsd.edu/papers/cogrob/cogrobhtml/cogrob-text.html
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/tacrfinalw-Grush.pdf
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/tacrfinalw-Grush.pdf
  http://wexler.free.fr/library/files/clark (1999) towards a cognitive robotics.pdf
  http://adb.sagepub.com/cgi/content/abstract/7/1/5
  
• Kerstin Dautenhahn, Bernard Ogden, Tom Quick & Tom Ziemke (2002). From embodied to socially embedded agents: Implications for interaction-aware robots.   (More)
  Additional links for this entry:
  http://linkinghub.elsevier.com/retrieve/pii/S1389041702000505
  http://www.ingentaconnect.com/content/els/13890417/2002/00000003/00000003/art00050
  
• Daniel C. Dennett (ms). Cog as a thought experiment.   (Abstract & more)
  Abstract: In her presentation at the Monte Verità workshop, Maja Mataric showed us a videotape of her robots cruising together through the lab, and remarked, aptly: "They're flocking, but that's not what they think they're doing." This is a vivid instance of a phenomenon that lies at the heart of all the research I learned about at Monte Verità: the execution of surprisingly successful "cognitive" behaviors by systems that did not explicitly represent, and did not need to explicitly represent, what they were doing. How "high" in the intuitive scale of cognitive sophistication can such unwitting prowess reach? All the way, apparently, since I want to echo Maja's observation with one of my own: "These roboticists are doing philosophy, but that's not what they think they're doing." It is possible, then, even to do philosophy--that most intellectual of activities--without realizing that that is what you are doing. It is even possible to do it well, for this is a good, new way of addressing antique philosophical puzzles
  Additional links for this entry:
  http://ase.tufts.edu/cogstud/papers/cogthogt.htm
  http://cogprints.ecs.soton.ac.uk/archive/00000248/
  http://cogprints.soton.ac.uk/documents/disk0/00/00/02/48/
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:248
  http://www.ingentaconnect.com/content/els/09218890/1997/00000020/00000002/art80709
  http://cogprints.org/248/1/cogthogt.htm
  http://cogprints.org/248/0/cogthogt.htm
  
• Matthew Elton (1997). Robots and rights: The ethical demands of artificial agents.   (More)
• James Gips (1994). Toward the ethical robot.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=216375
  http://www.roboethics.org/site/modules/mydownloads/download/references/Gips_EthicalRobot.pdf
  
• Owen Holland & Russell B. Goodman (2003). Robots with internal models: A route to machine consciousness?   (More)
  Additional links for this entry:
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1348
  http://www.ingentaconnect.com/content/imp/jcs/2003/00000010/F0020004/1342
  
• Hiroshi Ishiguro (2006). Android science: Conscious and subconscious recognition.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/758709863.pdf
  
• Nicola Lacey & M. Lee (2003). The epistemological foundations of artificial agents.   (Abstract & more)
  Abstract:   A situated agent is one which operates within an environment. In most cases, the environment in which the agent exists will be more complex than the agent itself. This means that an agent, human or artificial, which wishes to carry out non-trivial operations in its environment must use techniques which allow an unbounded world to be represented within a cognitively bounded agent. We present a brief description of some important theories within the fields of epistemology and metaphysics. We then discuss ways in which philosophical problems of scepticism are related to the problems faced by knowledge representation. We suggest that some of the methods that philosophers have developed to address the problems of epistemology may be relevant to the problems of representing knowledge within artificial agents
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=781081.781090
  http://www.springerlink.com/content/g6g408r7457k4u21/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5118862&PDF=1
  http://www.springerlink.com/index/G6G408R7457K4U21.pdf
  http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000003/05118862
  
• Christophe Menant (2005). Information and meaning in life, humans and robots (2005).   (Abstract & more)
  Abstract: Information and meaning exist around us and within ourselves, and the same information can correspond to different meanings. This is true for humans and animals, and is becoming true for robots. We propose here an overview of this subject by using a systemic tool related to meaning generation that has already been published (C. Menant, Entropy 2003). The Meaning Generator System (MGS) is a system submitted to a constraint that generates a meaningful information when it receives an incident information that has a relation with the constraint. The content of the meaningful information is explicited, and its function is to trigger an action that will be used to satisfy the constraint of the system. The MGS has been introduced in the case of basic life submitted to a "stay alive" constraint. We propose here to see how the usage of the MGS can be extended to more complex living systems, to humans and to robots by introducing new types of constraints, and integrating the MGS into higher level systems. The application of the MGS to humans is partly based on a scenario relative to the evolution of body self-awareness toward self-consciousness that has already been presented (C. Menant, Biosemiotics 2003, and TSC 2004). The application of the MGS to robots is based on the definition of the MGS applied to robots functionality, taking into account the origins of the constraints. We conclude with a summary of this overview and with themes that can be linked to this systemic approach on meaning generation
  Additional links for this entry:
  http://www.mdpi.org/fis2005/F.45.paper.pdf
  http://www.mdpi.net/fis2005/F.45.paper.pdf
  http://cogprints.org/4531/1/F.45.paper.pdf
  
• Marvin L. Minsky (1994). Will robots inherit the earth?   (Abstract & more)
  Abstract: Everyone wants wisdom and wealth. Nevertheless, our health often gives out before we achieve them. To lengthen our lives, and improve our minds, in the future we will need to change our our bodies and brains. To that end, we first must consider how normal Darwinian evolution brought us to where we are. Then we must imagine ways in which future replacements for worn body parts might solve most problems of failing health. We must then invent strategies to augment our brains and gain greater wisdom. Eventually we will entirely replace our brains -- using nanotechnology. Once delivered from the limitations of biology, we will be able to decide the length of our lives--with the option of immortality-- and choose among other, unimagined capabilities as well
  Additional links for this entry:
  http://www.media.mit.edu/~minsky/papers/sciam.inherit.txt
  http://www.media.mit.edu/~minsky/papers/sciam.inherit.html
  http://web.media.mit.edu/~minsky/papers/sciam.inherit.html
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=7939559&dopt=Citation
  
• Hans Moravec (online). Bodies, robots, minds.   (Abstract & more)
  Abstract: Serious attempts to build thinking machines began after the second world war. One line of research, called Cybernetics, used electronic circuitry imitating nervous systems to make machines that learned to recognize simple patterns, and turtle-like robots that found their way to recharging plugs. A different approach, named Artificial Intelligence, harnessed the arithmetic power of post-war computers to abstract reasoning, and by the 1960s made computers prove theorems in logic and geometry, solve calculus problems and play good games of checkers. At the end of the 1960s, research groups at MIT and Stanford attached television cameras and robot arms to their computers, so "thinking" programs could begin to collect information directly from the real world
• Hans Moravec (online). Robotics.   (Abstract & more)
  Abstract: the development of machines with motor, perceptual and cognitive skills once found only in animals and humans. The field parallels and has adopted developments from several areas, among them mechanization, automation and artificial intelligence, but adds its own gripping myth, of complete artificial mechanical human beings. Ancient images and figurines depicting animals and humans can be interpreted as steps towards this vision, as can mechanical automata from classical times on. The pace accelerated rapidly in the twentieth century with the development of electronic sensing and amplification that permitted automata to sense and react as well as merely perform. By the late twentieth century automata controlled by computers could also think and remember
• Hans Moravec (online). Robots inherit human minds.   (Abstract & more)
  Abstract: Our first tools, sticks and stones, were very different from ourselves. But many tools now resemble us, in function or form, and they are beginning to have minds. A loose parallel with our own evolution suggests how they may develop in future. Computerless industrial machinery exhibits the behavioral flexibility of single-celled organisms. Today's best computer-controlled robots are like the simpler invertebrates. A thousand-fold increase in computer power in this decade should make possible machines with reptile-like sensory and motor competence. Growing computer power over the next half century will allow robots that learn like mammals, model their world like primates and eventually reason like humans. Depending on your point of view, humanity will then have produced a worthy successor, or transcended inherited limitations and transformed itself into something quite new. No longer limited by the slow pace of human learning and even slower biological evolution, intelligent machinery will conduct its affairs on an ever faster, ever smaller scale, until coarse physical nature has been converted to fine-grained purposeful thought
• Hans Moravec (1994). The age of robots.   (Abstract & more)
  Abstract: _Our artifacts are getting smarter, and a loose parallel with the evolution of animal intelligence suggests one future course_ _for them. Computerless industrial machinery exhibits the behavioral flexibility of single-celled organisms. Today's best_ _computer-controlled robots are like the simpler invertebrates. A thousand-fold increase in computer power in this decade_ _should make possible machines with reptile-like sensory and motor competence. Properly configured, such robots could_ _do in the physical world what personal computers now do in the world of data--act on our behalf as literal-minded_ _slaves. Growing computer power over the next half-century will allow this reptile stage will be surpassed, in stages_ _producing robots that learn like mammals, model their world like primates and eventually reason like humans._ _Depending on your point of view, humanity will then have produced a worthy successor, or transcended inherited_ _limitations and transformed itself into something quite new. No longer limited by the slow pace of human learning and_ _even slower biological evolution, intelligent machinery will conduct its affairs on an ever faster, ever smaller scale, until_ _coarse physical nature has been converted to fine-grained purposeful thought._
  Additional links for this entry:
  http://www.ri.cmu.edu/pubs/pub_1810_text.html
  http://www.frc.ri.cmu.edu/~hpm/project.archive/general.articles/1993/Robot93.html
  
• Stephen Petersen (2007). The ethics of robot servitude.   (Abstract & more)
  Abstract: Assume we could someday create artificial creatures with intelligence comparable to our own. Could it be ethical use them as unpaid labor? There is very little philosophical literature on this topic, but the consensus so far has been that such robot servitude would merely be a new form of slavery. Against this consensus I defend the permissibility of robot servitude, and in particular the controversial case of designing robots so that they want to serve (more or less particular) human ends. A typical objection to this case draws an analogy to the genetic engineering of humans: if designing eager robot servants is permissible, it should also be permissible to design eager human servants. Few ethical views can easily explain even the wrongness of such human engineering, however, and those few explanations that are available break the analogy with engineering robots. The case turns out to be illustrative of profound problems in the field of population ethics.
  Additional links for this entry:
  http://stevepetersen.net/professional/petersen-robot-servitude-slides.pdf
  http://stevepetersen.net/styleless/professional/petersen-robot-servitude.pdf
  http://journalsonline.tandf.co.uk/smpp/content~content=a770959968~db=all~jumptype=rss
  http://taylorandfrancis.metapress.com/index/V14W2MT8346M88N1.pdf
  http://www.informaworld.com/index/V14W2MT8346M88N1.pdf
  
• C. T. A. Schmidt & F. Kraemer (2006). Robots, Dennett and the autonomous: A terminological investigation.   (Abstract & more)
  Abstract: In the present enterprise we take a look at the meaning of Autonomy, how the word has been employed and some of the consequences of its use in the sciences of the artificial. Could and should robots really be autonomous entities? Over and beyond this, we use concepts from the philosophy of mind to spur on enquiry into the very essence of human autonomy. We believe our initiative, as does Dennett's life-long research, sheds light upon the problems of robot design with respect to their relation with humans
  Additional links for this entry:
  http://www.springerlink.com/content/j20034x2925vk488/fulltext.pdf
  http://www.springerlink.com/index/J20034X2925VK488.pdf
  

Computationalism

• Roberto Cordeschi, M. Frixione, Roberto Cordeschi & M. Frixione (online). Computationalism under attack.   (Abstract & more)
  Abstract: in M. Marraffa, M. De Caro and F. Ferretti (eds.), Cartographies of the Mind: Philosophy and Psychology in Intersection, Springer, Berlin-Heidelberg, 2007, pp. 37-49. PDF
• Michael G. Dyer (1990). Intentionality and computationalism: Minds, machines, Searle and Harnad.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/777957609.pdf
  
• Larry Hauser (2000). Ordinary devices: Reply to Bringsjord's Clarifying the Logic of Anti-Computationalism: Reply to Hauser.   (Abstract & more)
  Abstract: What Robots Can and Can't Be (hereinafter Robots) is, as Selmer Bringsjord says "intended to be a collection of formal-arguments-that-border-on-proofs for the proposition that in all worlds, at all times, machines can't be minds" (Bringsjord, forthcoming). In his (1994) "Précis of What Robots Can and Can't Be" Bringsjord styles certain of these arguments as proceeding "repeatedly . . . through instantiations of" the "simple schema"
  Additional links for this entry:
  http://www.wutsamada.com/work/ordinary.htm
  http://portal.acm.org/citation.cfm?id=596715.596855
  http://www.springerlink.com/content/content/h874662818vp4p8x/fulltext.pdf
  http://www.springerlink.com/content/h874662818vp4p8x/fulltext.pdf
  http://www.springerlink.com/index/H874662818VP4P8X.pdf
  
• Geoffrey Laforte, Pat Hayes & Kenneth M. Ford (1998). Why Godel's theorem cannot refute computationalism: A reply to Penrose.   (More)
• Gualtiero Piccinini (2009). Computationalism in the philosophy of mind.   (Abstract & more)
  Abstract: Computationalism has been the mainstream view of cognition for decades. There are periodic reports of its demise, but they are greatly exaggerated. This essay surveys some recent literature on computationalism and reaches the following conclusions. Computationalism is a family of theories about the mechanisms of cognition. The main relevant evidence for testing computational theories comes from neuroscience, though psychology and AI are relevant too. Computationalism comes in many versions, which continue to guide competing research programs in philosophy of mind as well as psychology and neuroscience. Although our understanding of computationalism has deepened in recent years, much work in this area remains to be done
  Additional links for this entry:
  http://www3.interscience.wiley.com/cgi-bin/fulltext/122301402/PDFSTART
  
• Gualtiero Piccinini (2007). Computationalism, the church–turing thesis, and the church–turing fallacy.   (Abstract & more)
  Abstract: The Church–Turing Thesis (CTT) is often employed in arguments for computationalism. I scrutinize the most prominent of such arguments in light of recent work on CTT and argue that they are unsound. Although CTT does nothing to support computationalism, it is not irrelevant to it. By eliminating misunderstandings about the relationship between CTT and computationalism, we deepen our appreciation of computationalism as an empirical hypothesis.
  Additional links for this entry:
  http://www.springerlink.com/content/u2914611854801u1/fulltext.pdf
  
• Michael Rescorla (2007). Church's Thesis and the Conceptual Analysis of Computability.   (Abstract & more)
  Abstract: Church's thesis asserts that a number-theoretic function is intuitively computable if and only if it is recursive. A related thesis asserts that Turing's work yields a conceptual analysis of the intuitive notion of numerical computability. I endorse Church's thesis, but I argue against the related thesis. I argue that purported conceptual analyses based upon Turing's work involve a subtle but persistent circularity. Turing machines manipulate syntactic entities. To specify which number-theoretic function a Turing machine computes, we must correlate these syntactic entities with numbers. I argue that, in providing this correlation, we must demand that the correlation itself be computable. Otherwise, the Turing machine will compute uncomputable functions. But if we presuppose the intuitive notion of a computable relation between syntactic entities and numbers, then our analysis of computability is circular.
  Additional links for this entry:
  http://www.projecteuclid.org/DPubS?verb=Display&version=1.0&service=UI&handle=euclid.ndjfl/1179323267&page=record
  

Philosophy of AI, Miscellaneous

• Varol Akman (2000). Introduction to the special issue on philosophical foundations of artificial intelligence.   (Abstract & more)
  Abstract: This is the guest editor's introduction to a JETAI special issue on philosophical foundations of AI
  Additional links for this entry:
  http://citeseer.ist.psu.edu/346061.html
  http://cogprints.ecs.soton.ac.uk/archive/00000961/
  http://citeseer.ist.psu.edu/akman00introduction.html
  http://www.cs.bilkent.edu.tr/~akman/jour-papers/jetai/jetai2000.pdf
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:961
  http://www.informaworld.com/index/K7H3RFR1N065UAU2.pdf
  http://www.ingentaconnect.com/content/tandf/teta/2000/00000012/00000003/art00001
  http://cogprints.org/961/2/intro.ps
  http://cogprints.org/961/0/intro.ps
  
• Mario Alai (2004). A.I., Scientific discovery and realism.   (Abstract & more)
  Abstract: Epistemologists have debated at length whether scientific discovery is a rational and logical process. If it is, according to the Artificial Intelligence hypothesis, it should be possible to write computer programs able to discover laws or theories; and if such programs were written, this would definitely prove the existence of a logic of discovery. Attempts in this direction, however, have been unsuccessful: the programs written by Simon's group, indeed, infer famous laws of physics and chemistry; but having found no new law, they cannot properly be considered discovery machines. The programs written in the Turing tradition, instead, produced new and useful empirical generalization, but no theoretical discovery, thus failing to prove the logical character of the most significant kind of discoveries. A new cognitivist and connectionist approach by Holland, Holyoak, Nisbett and Thagard, looks more promising. Reflection on their proposals helps to understand the complex character of discovery processes, the abandonment of belief in the logic of discovery by logical positivists, and the necessity of a realist interpretation of scientific research
  Additional links for this entry:
  http://www.cse.buffalo.edu/~rapaport/510/mm-alai.pdf
  http://www.springerlink.com/content/u078803u28817475/fulltext.pdf
  http://www.springerlink.com/index/U078803U28817475.pdf
  http://www.ingentaconnect.com/content/klu/mind/2004/00000014/00000001/05142090
  
• Anthony F. Beavers (2002). Phenomenology and artificial intelligence.   (Abstract & more)
  Abstract: In CyberPhilosophy: The Intersection of Philosophy and Computing, edited by James H. Moor and Terrell Ward Bynum (Oxford, UK: Blackwell, 2002), 66-77. Also in Metaphilosophy 33.1/2 (2002): 70-82
  Additional links for this entry:
  http://www.blackwell-synergy.com/links/doi/10.1111/1467-9973.00217/abs/
  http://www.blackwell-synergy.com/doi/abs/10.1111/1467-9973.00217
  http://www.ingentaconnect.com/content/bpl/meta/2002/00000033/F0020001/art00217
  
• Nick Bostrom (1998). How long before superintelligence?   (Abstract & more)
  Abstract: _This paper outlines the case for believing that we will have superhuman artificial intelligence_ _within the first third of the next century. It looks at different estimates of the processing power of_ _the human brain; how long it will take until computer hardware achieve a similar performance;_ _ways of creating the software through bottom-up approaches like the one used by biological_ _brains; how difficult it will be for neuroscience figure out enough about how brains work to_ _make this approach work; and how fast we can expect superintelligence to be developed once_ _there is human-level artificial intelligence._
• Nick Bostrom (online). The transhumanist FAQ.   (More)
  Additional links for this entry:
  http://www.transhumanism.org/resources/faq.html
  
• Rodney Brooks (2001). The relationship between matter and life.   (Abstract & more)
  Abstract: Researchers in artificial intelligence (AI) Moore’s law states that computational complexity of the models is still far below that and artificial life (Alife) are interested resources for a fixed price roughly double of any living system. New experiments in evo- in understanding the properties of liv- every 18 months. From about 1975 into the lution simulate spatially isolated populations ing organisms so that they can build artificial early 1990s all the gains of Moore’s law went to investigate speciation. Over the past few systems that exhibit these properties for into the changeover from the centralized years, new directions have emerged in AI⁵, in useful purposes. AI researchers are interest- mainframe to the individual computer on attempts to implement artificial creatures in ed mostly in perception, cognition and your desk, accommodating a vastly simulated or physical environments. generation of action (Box 1), whereas Alife increased number of users. The amount of Often called the behaviour-based focuses on evolution, reproduction, computing power available to the individual approach, this new mode of thought involves morphogenesis and metabolism (Box 2). scientist did not change that much, although the connection of perception to action with Neither of these disciplines is a conventional the price came down by a factor of a little in the way of intervening representa- science; rather, they are a mixture of science thousand. But since the early 1990s, all of tional systems. Rather than relying on and engineering. Despite, or perhaps Moore’s law has gone into increasing the per- search, this approach relies on the correct because of, this hybrid structure, both disci- formance of the workstation itself. short, fast connections being present plines have been very successful and our And both AI and Alife have benefited from between sensory and motor modules. world is full of their products. this shift. Behaviour-based approaches began with Every time we use a computer we use Increased computer power has enabled insect models, but more recently they have algorithms and techniques developed by AI search-based AI to push ahead with been extended to humanoid robots⁶ — researchers.
  Additional links for this entry:
  http://www.nature.com/nature/journal/v409/n6818/full/409409a0.html
  http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=11201756&dopt=Citation
  
• Andy Clark (2003). Artificial intelligence and the many faces of reason.   (Abstract & more)
  Abstract: wide variety of things. It covers the capacity to carry out deductive inferences, to make
  Additional links for this entry:
  http://www.philosophy.ed.ac.uk/staff/clark/pubs/AIandFacesofReason.pdf
  http://books.google.com/books?hl=en&lr=&id=oI8lWo-dasIC&oi=fnd&pg=PA309&ots=YXROBOGM7A&sig=a69b_8ZipUXmv8jfu6-lWPxYp4U
  
• Roberto Cordeschi (2007). AI turns fifty: Revisiting its origins.   (Abstract & more)
  Abstract: Applied Artificial Intelligence, 21, 2007, pp. 259-279
  Additional links for this entry:
  http://www.informaworld.com/index/778482823.pdf
  
• Roberto Cordeschi (2006). Searching in a Maze, in search of knowledge: Issues in early artificial intelligence.   (Abstract & more)
  Abstract: Lecture Notes in Artificial Intelligence, vol. 4155, Springer, Berlin-Heidelberg, 2006, pp. 1-23. PDF
  Additional links for this entry:
  http://www.springerlink.com/index/k151241455035325.pdf
  
• Hubert L. Dreyfus (1985). From socrates to expert systems: The limits and dangers of calculative rationality.   (Abstract & more)
  Abstract: Actual AI research began auspiciously around 1955 with Allen Newell and Herbert Simon's work at the RAND Corporation. Newell and Simon proved that computers could do more than calculate. They demonstrated that computers were physical symbol systems whose symbols could be made to stand for anything, including features of the real world, and whose programs could be used as rules for relating these features. In this way computers could be used to simulate certain important aspects intelligence. Thus the information-processing model of the mind was born. But, looking back over these fifty years, it seems that theoretical AI with its promise of a robot like HAL appears to be a perfect example of what Imre Lakatos has called a "degenerating research program"
  Additional links for this entry:
  http://ist-socrates.berkeley.edu/~hdreyfus/html/paper_socrates.html
  
• B. Elan Dresher & Norbert Hornstein (1976). On some supposed contributions of artificial intelligence to the scientific study of language.   (More)
• Włodzisław Duch (2007). What is computational intelligence and where is it going?   (Abstract & more)
  Abstract: What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
  Additional links for this entry:
  http://www.springerlink.com/index/k275703555734407.pdf
  
• Susan L. Epstein (1992). The role of memory and concepts in learning.   (Abstract & more)
  Abstract: The extent to which concepts, memory, and planning are necessary to the simulation of intelligent behavior is a fundamental philosophical issue in Artificial Intelligence. An active and productive segement of the AI community has taken the position that multiple low-level agents, properly organized, can account for high-level behavior. Empirical research on these questions with fully operational systems has been restricted to mobile robots that do simple tasks. This paper recounts experiments with Hoyle, a system in a cerebral, rather than a physical, domain. The program learns to perform well and quickly, often outpacing its human creators at two-person, perfect information board games. Hoyle demonstrates that a surprising amount of intelligent behavior can be treated as if it were situation-determined, that often planning is unnecessary, and that the memory required to support this learning is minimal. Concepts, however, are crucial to this reactive program's ability to learn and perform
  Additional links for this entry:
  http://www.springerlink.com/content/q764210h47533476/fulltext.pdf
  http://www.springerlink.com/index/Q764210H47533476.pdf
  
• Tom Froese (2007). On the role of AI in the ongoing paradigm shift within the cognitive sciences.   (Abstract & more)
  Abstract: This paper supports the view that the ongoing shift from orthodox to embodied-embedded cognitive science has been significantly influenced by the experimental results generated by AI research. Recently, there has also been a noticeable shift toward enactivism, a paradigm which radicalizes the embodied-embedded approach by placing autonomous agency and lived subjectivity at the heart of cognitive science. Some first steps toward a clarification of the relationship of AI to this further shift are outlined. It is concluded that the success of enactivism in establishing itself as a mainstream cognitive science research program will depend less on progress made in AI research and more on the development of a phenomenological pragmatics
  Additional links for this entry:
  http://froese.files.wordpress.com/2007/06/on-the-role-of-ai-in-the-ongoing-paradigm-shift.pdf
  
• Patrick J. Hayes, Kenneth M. Ford & J. R. Adams-Webber (1994). Human reasoning about artificial intelligence.   (More)
  Additional links for this entry:
  http://www.informaworld.com/index/777592360.pdf
  
• Brian L. Keeley (1994). Against the global replacement: On the application of the philosophy of artificial intelligence to artificial life.   (More)
• Drew McDermott (1997). How intelligent is deep blue?   (More)
• James H. Moor (1998). Assessing artificial intelligence and its critics.   (More)
• John Pollock (online). Oscar: A cognitive architecture for intelligent agents.   (Abstract & more)
  Abstract: The “grand problem” of AI has always been to build artificial agents of human-level intelligence, capable of operating in environments of real-world complexity. OSCAR is a cognitive architecture for such agents, implemented in LISP. OSCAR is based on my extensive work in philosophy concerning both epistemology and rational decision making. This paper provides a detailed overview of OSCAR. The main conclusions are that such agents must be capablew of operating against a background of pervasive ignorance, because the real world is too complex for them to know more than a small fraction of what is true. This is handled by giving the agent the power to reason defeasibily. The OSCAR system of defeasible reasoning is sketched. It is argued that if epistemic cognition must be defeasible, planning must also be done defeasibly, and the best way to do that is to reason defeasibly about plans. A sketch is given about how this might work
  Additional links for this entry:
  http://oscarhome.soc-sci.arizona.edu/ftp/PAPERS/OSCAR architecture.pdf
  
• John L. Pollock (1999). Rational cognition in Oscar.   (Abstract & more)
  Abstract: Stuart Russell [14] describes rational agents as --œthose that do the right thing--�. The problem of designing a rational agent then becomes the problem of figuring out what the right thing is. There are two approaches to the latter problem, depending upon the kind of agent we want to build. On the one hand, anthropomorphic agents are those that can help human beings rather directly in their intellectual endeavors. These endeavors consist of decision making and data processing. An agent that can help humans in these enterprises must make decisions and draw conclusions that are rational by human standards of rationality. Anthropomorphic agents can be contrasted with goal-oriented agents --” those that can carry out certain narrowly-defined tasks in the world. Here the objective is to get the job done, and it makes little difference how the agent achieves its design goal
  Additional links for this entry:
  http://citeseer.ist.psu.edu/246569.html
  http://portal.acm.org/citation.cfm?id=749598
  http://oscarhome.soc-sci.arizona.edu/ftp/PAPERS/Rational Cognition in OSCAR.ps
  http://oscarhome.soc-sci.arizona.edu/ftp/OSCAR-web-page/PAPERS/Rational-Cognition-in-OSCAR.pdf
  http://books.google.com/books?hl=en&lr=&id=pxKPVyzPFEMC&oi=fnd&pg=PA71&ots=RQ2LB5zx9I&sig=evRmQuZSv9eq2Wxij231iMjAfzA
  http://www.springerlink.com/index/a7r273h735877q41.pdf
  
• John L. Pollock (online). The Oscar project.   (More)
• Viola Schiaffonati (2003). A framework for the foundation of the philosophy of artificial intelligence.   (Abstract & more)
  Abstract:   The peculiarity of the relationship between philosophy and Artificial Intelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must be considered. Moreover, this framework is revised and extended in the light of the consideration of a type of multiagent system devoted to afford the issue of scientific discovery both from a conceptual and from a practical point of view
  Additional links for this entry:
  http://www.cse.buffalo.edu/~rapaport/Papers/Papers.by.Others/mm.viola.pdf
  http://www.springerlink.com/content/tun11774g3434j44/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5142084&PDF=1
  http://www.springerlink.com/index/TUN11774G3434J44.pdf
  http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000004/05142084
  
• Herbert A. Simon (1995). Machine as mind.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=216350.216358
  
• Aaron Sloman (2002). The irrelevance of Turing machines to artificial intelligence.   (More)
  Additional links for this entry:
  http://lib.org.by/_djvu/Cs_Computer science/CsAi_AI, knowledge/Scheutz M. (ed.) Computationalism (MIT, 2002)(223s).pdf#page=101
  
• Robert Sparrow (2002). The March of the robot dogs.   (Abstract & more)
  Abstract: The Centre for Applied Philosophy and Public Ethics (CAPPE) was established in 2000 as a Special Research Centre in applied philosophy funded by the Australian Research Council. It has combined the complementary strengths of two existing centres specialising in applied philosophy, namely the Centre for Philosophy and Public Issues (CPPI) at the University of Melbourne and the Centre for Professional and Applied Ethics at Charles Sturt University. It operates as a unified centre with two divisions: in Melbourne at the University of Melbourne and in Canberra at Charles Sturt University. The Director of CAPPE and the head of the Canberra node is Professor Seumas Miller. Professor C.A.J. (Tony) Coady is the Deputy Director of CAPPE and the head of the Melbourne node
  Additional links for this entry:
  http://www.cs.pitt.edu/~bigrigg/cs1590/sparrow.pdf
  http://www.cs.cmu.edu/~social/reading/Sparrow1.pdf
  http://portal.acm.org/citation.cfm?id=607976.607992
  http://www-2.cs.cmu.edu/~social/reading/Sparrow1.pdf
  http://eprints.infodiv.unimelb.edu.au/archive/00000138
  http://eprints.unimelb.edu.au/archive/00000138/01/Sparrow1.pdf
  http://www.springerlink.com/content/lu2520h1307q6772/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5111338&PDF=1
  http://www.springerlink.com/index/LU2520H1307Q6772.pdf
  http://www.ingentaconnect.com/content/klu/etin/2002/00000004/00000004/05111338
  
• Paul R. Thagard (1986). Parallel computation and the mind-body problem.   (More)
  Additional links for this entry:
  http://www.leaonline.com/doi/pdfplus/10.1207/s15516709cog1003_3
  http://www.cogsci.rpi.edu/CSJarchive/1986v10/i03/p0301p0318/MAIN.PDF
  
• Kristinn R. Thórisson (2007). Integrated A.I. Systems.   (Abstract & more)
  Abstract: The broad range of capabilities exhibited by humans and animals is achieved through a large set of heterogeneous, tightly integrated cognitive mechanisms. To move artificial systems closer to such general-purpose intelligence we cannot avoid replicating some subset—quite possibly a substantial portion—of this large set. Progress in this direction requires that systems integration be taken more seriously as a fundamental research problem. In this paper I make the argument that intelligence must be studied holistically. I present key issues that must be addressed in the area of integration and propose solutions for speeding up rate of progress towards more powerful, integrated A.I. systems, including (a) tools for building large, complex architectures, (b) a design methodology for building realtime A.I. systems and (c) methods for facilitating code sharing at the community level
  Additional links for this entry:
  http://www.springerlink.com/content/y04485h9j3u8x431/fulltext.pdf
  http://www.springerlink.com/index/Y04485H9J3U8X431.pdf
  http://www.ingentaconnect.com/content/klu/mind/2007/00000017/00000001/00009055
  
• Larry Hauser (online). Artificial intelligence.   (More)
• Vernor Vinge (online). The technological singularity.   (More)
  Additional links for this entry:
  http://wholeearthmag.com/ArticleBin/111-3.pdf
  http://adsabs.harvard.edu/abs/1993vise.nasa...11V
  http://www.aleph.se/Trans/Global/Singularity/sing.html
  http://www.cs.ucsd.edu/users/goguen/misc/singularity.html
  http://www.kurzweilai.net/articles/art0092.html?printable=1
  http://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html
  http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=N9427359AH