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Philosophy of artificial intelligence :: Computation and representation

• Symbols and symbol systems [15]
• Computational semantics [12]
• Implicit/explicit rules and representations [10]
• AI without representation? [10]
• Computation and representation, misc [6]

Symbols and symbol systems

See also: Computational semantics, Implicit/explicit rules and representations, AI without representation?, Computation and representation, misc, Subsymbolic computation, Language and thought, The language of thought, Information-based accounts.

• 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:
  https://www.era.lib.ed.ac.uk/handle/1842/1439?mode=simple
  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://www.ingentaconnect.com/content/routledg/cphp/2006/00000019/00000003/art00002
  http://taylorandfrancis.metapress.com/index/XNRG5M77H65766ML.pdf
  http://www.informaworld.com/index/747697830.pdf
  
• 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.org/1583/0/harnad92.symbol.object.html
  http://cogprints.org/1583/00/harnad92.symbol.object.html
  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
  
• 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.org/2133/00/harnad02.symlang.htm
  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 (1993). 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.org/1588/00/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
  
• 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://cogprints.org/3106/01/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/615/0/The_Symbol_Grounding_Problem.html
  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
  
• Karl MacDorman (1997). How to ground symbols adaptively.   (More)
• Allen Newell & Herbert 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://www.phil.canterbury.ac.nz/tom_bestor/e-texts/Newell and Simon - CS as Empirical Inquiry (1975) p113-newell.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=E4xIcIdzXt&sig=x8TqXd03sMF7Ku2tjTjKPLStp7M
  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=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=E5oF7DeyQy&sig=cEaNBtdTsZioMpkk6W2Yd7_Ecf8
  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=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.amacad.org/publications/fall2004/pinker.pdf
  https://www.amacad.org/publications/fall2004/pinker.pdf
  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 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
  
• 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://citeseer.comp.nus.edu.sg/rd/0,285913,1,0.25,Download/http:qSqqSqwww.cogsci.rpi.eduqSq~rsunqSqsun.PP00.ps
  http://citeseer.comp.nus.edu.sg/compress/0/papers/cs/13170/http:zSzzSzwww.cecs.missouri.eduzSz~rsunzSzsun.PP99.ps.gz/sun99symbol.ps
  http://www.informaworld.com/smpp/./ftinterface~content=a713690432~fulltext=713240930
  http://www.ingentaconnect.com/content/routledg/cphp/2000/00000013/00000002/art00001
  http://taylorandfrancis.metapress.com/index/KKLQAQTYVG4RUY7G.pdf
  http://www.informaworld.com/index/KKLQAQTYVG4RUY7G.pdf
  
• 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

See also: The Chinese room, Symbols and symbol systems, Computation and representation, misc, The language of thought, Machine functionalism.

• Varol Akman (1998). Situations and artificial intelligence.   (More)
• Radu 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
• Daniel 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 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://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://www.ecs.soton.ac.uk/~harnad/Temp/darwin.htm
  http://cogprints.ecs.soton.ac.uk/archive/00003016/
  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
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:7715
  
• Philip 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 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)
• 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://cogprints.org/200/0/sig.ps
  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
  
• W. Woods (1981). Procedural semantics as a theory of meaning.   (More)

Implicit/explicit rules and representations

See also: Symbols and symbol systems, AI without representation?, Representation in connectionism, Beliefs, Rule-following, Representation.

• William 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
  Additional links for this entry:
  http://mechanism.ucsd.edu/~bill/research/situatedcognitionpaper.webversion.pdf
  
• Andy Clark (1991). In defense of explicit rules.   (More)
• Daniel 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
  
• Robert 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.ingentaconnect.com/content/klu/mind/1998/00000008/00000003/00160303
  http://www.springerlink.com/content/j4u0011h11660267/fulltext.pdf
  http://www.springerlink.com/index/J4U0011H11660267.pdf
  
• Lawrence 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 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://consc.net/cgi/go.fcgi?id=SKOCCC&free=0&u=http://www.springerlink.com/index/J821320368HT5863.pdf
  http://www.consc.net/cgi/go.fcgi?id=SKOCCC&free=0&u=http://www.springerlink.com/index/J821320368HT5863.pdf
  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).