MindPapers: 6.4. Special Topics in AI

Buchanan, Bruce G. (1988). AI as an experimental science. In James H. Fetzer (ed.), Aspects of AI. Kluwer. (Google | Edit)

Bundy, A. (1990). What kind of field is AI? In Derek Partridge & Y. Wilks (eds.), The Foundations of Artificial Intelligence: A Sourcebook. Cambridge University Press. (Cited by 6 | Google | Edit)

Dennett, Daniel C. (1978). AI as philosophy and as psychology. In Martin Ringle (ed.), Philosophical Perspectives on Artificial Intelligence. Humanities Press. (Annotation | Google | Edit)

Glymour, C. (1988). AI is philosophy. In James H. Fetzer (ed.), Aspects of AI. D. (Cited by 1 | Google | Edit)

Harre, Rom (1990). Vigotsky and artificial intelligence: What could cognitive psychology possibly be about? Midwest Studies in Philosophy 15:389-399. (Google | Edit)

Kukla, André (1989). Is AI an empirical science? Analysis 49 (March):56-60. (Cited by 4 | Annotation | Google | Edit)

Kukla, André (1994). Medium AI and experimental science. Philosophical Psychology 7 (4):493-5012. (Cited by 4 | Annotation | Google | Edit)

McCarthy, John (online). What is artificial intelligence? (Cited by 38 | Google | More links | Edit)

Minsky, Marvin L. (online). From pain to suffering. (Google | Edit)

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]

Nakashima, H. (1999). AI as complex information processing. Minds and Machines 9 (1):57-80. (Cited by 2 | Google | More links | Edit)

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

Sloman, Aaron (2002). The irrelevance of Turing machines to AI. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press. (Cited by 9 | Google | More links | Edit)

Sufka, Kenneth J. & Polger, Thomas W. (2005). Closing the gap on pain. In Murat Aydede (ed.), Pain: New Essays on its Nature and the Methodology of its Study. MIT Press. (Google | More links | Edit)

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?

Yudkowsky, Eliezer (online). General intelligence and seed AI. (Google | Edit)

Anselme, Patrick & French, Robert M. (1999). Interactively converging on context-sensitive representations: A solution to the frame problem. Revue Internationale de Philosophie 53 (209):365-385. (Google | Edit)

Abstract: While we agree that the frame problem, as initially stated by McCarthy and Hayes (1969), is a problem that arises because of the use of representations, we do not accept the anti-representationalist position that the way around the problem is to eliminate representations. We believe that internal representations of the external world are a necessary, perhaps even a defining feature, of higher cognition. We explore the notion of dynamically created context-dependent representations that emerge from a continual interaction between working memory, external input, and long-term memory. We claim that only this kind of representation, necessary for higher cognitive abilities such as counterfactualization, will allow the combinatorial explosion inherent in the frame problem to be avoided

Clark, Andy (2002). Global abductive inference and authoritative sources, or, how search engines can save cognitive science. Cognitive Science Quarterly 2 (2):115-140. (Cited by 2 | Google | More links | Edit)

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

Dennett, Daniel C. (1984). Cognitive wheels: The frame problem of AI. In Minds, Machines and Evolution. Cambridge University Press. (Cited by 139 | Annotation | Google | Edit)

Dreyfus, Hubert L. & Dreyfus, Stuart E. (1987). How to stop worrying about the frame problem even though it's computationally insoluble. In Zenon W. Pylyshyn (ed.), The Robot's Dilemma. Ablex. (Annotation | Google | Edit)

Fetzer, James H. (1990). The frame problem: Artificial intelligence meets David Hume. International Journal of Expert Systems 3:219-232. (Cited by 13 | Google | More links | Edit)

Fodor, Jerry A. (1987). Modules, frames, fridgeons, sleeping dogs, and the music of the spheres. In Zenon W. Pylyshyn (ed.), The Robot's Dilemma. Ablex. (Cited by 56 | Google | Edit)

Fodor, Jerry A. (1989). Modules, frames, fridgeons, sleeping dogs. In Modularity in Knowledge Representation and Natural-Language Understanding. Cambridge: MIT Press. (Google | Edit)

Fodor, Jerry A. (1987). Modules, frames, fridgeons. In Modularity In Knowledge Representation And Natural-Language Understanding. Cambridge: Mit Press. (Google | Edit)

Haugeland, John (1987). An overview of the frame problem. In Zenon W. Pylyshyn (ed.), The Robot's Dilemma. Ablex. (Cited by 17 | Annotation | Google | Edit)

Hayes, Patrick (1987). What the frame problem is and isn't. In Zenon W. Pylyshyn (ed.), The Robot's Dilemma. Ablex. (Cited by 25 | Annotation | Google | Edit)

Hendricks, Scott (2006). The frame problem and theories of belief. Philosophical Studies 129 (2):317-33. (Google | More links | Edit)

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

Janlert, Lars-Erik (1987). Modeling change: The frame problem. In Zenon W. Pylyshyn (ed.), The Robot's Dilemma. Ablex. (Cited by 23 | Google | Edit)

Korb, Kevin B. (1998). The frame problem: An AI fairy tale. Minds and Machines 8 (3):317-351. (Cited by 1 | Google | More links | Edit)

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

Lormand, Eric (1990). Framing the frame problem. Synthese 82 (3):353-74. (Cited by 9 | Annotation | Google | More links | Edit)

Abstract: The frame problem is widely reputed among philosophers to be one of the deepest and most difficult problems of cognitive science. This paper discusses three recent attempts to display this problem: Dennett's problem of ignoring obviously irrelevant knowledge, Haugeland's problem of efficiently keeping track of salient side effects, and Fodor's problem of avoiding the use of kooky concepts. In a negative vein, it is argued that these problems bear nothing but a superficial similarity to the frame problem of AI, so that they do not provide reasons to disparage standard attempts to solve it. More positively, it is argued that these problems are easily solved by slight variations on familiar AI themes. Finally, some discussion is devoted to more difficult problems confronting AI

Lormand, Eric (1998). The frame problem. In Robert A. Wilson & Frank F. Keil (eds.), MIT Encyclopedia of the Cognitive Sciences (MITECS). MIT Press. (Google | Edit)

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

Lormand, Eric (1994). The holorobophobe's dilemma. In Kenneth M. Ford & Z. Pylylshyn (eds.), The Robot's Dilemma Revisited. Ablex. (Cited by 2 | Google | More links | Edit)

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

Maloney, J. Christopher (1988). In praise of narrow minds. In James H. Fetzer (ed.), Aspects of AI. D. (Google | Edit)

McCarthy, John & Hayes, Patrick (1969). Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer & Donald Michie (eds.), Machine Intelligence 4. Edinburgh University Press. (Cited by 1919 | Google | More links | Edit)

McDermott, Drew (1987). We've been framed: Or, why AI is innocent of the frame problem. In Zenon W. Pylyshyn (ed.), The Robot's Dilemma. Ablex. (Cited by 15 | Annotation | Google | Edit)

Murphy, Dominic (2001). Folk psychology meets the frame problem. Studies in History and Philosophy of Modern Physics 32 (3):565-573. (Google | Edit)

Pollock, John L. (1997). Reasoning about change and persistence: A solution to the frame problem. Noûs 31 (2):143-169. (Cited by 4 | Google | More links | Edit)

Pylyshyn, Zenon (1996). The frame problem blues. Once more, with feeling. In K. M. Ford & Z. W. Pylyshyn (eds.), The Robot's Dilemma Revisited: The Frame Problem in Artificial Intelligence. Ablex. (Cited by 2 | Google | More links | Edit)

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”

Pylyshyn, Zenon W. (ed.) (1987). The Robot's Dilemma. Ablex. (Cited by 148 | Annotation | Google | More links | Edit)

Shanahan, Murray & Baars, Bernard J. (2005). Applying global workspace theory to the frame problem. Cognition 98 (2):157-176. (Cited by 28 | Google | More links | Edit)

Waskan, Jonathan A. (2000). A virtual solution to the frame problem. Proceedings of the First IEEE-RAS International Conference on Humanoid Robots. (Cited by 1 | Google | Edit)

Abstract: We humans often respond effectively when faced with novel circumstances. This is because we are able to predict how particular alterations to the world will play out. Philosophers, psychologists, and computational modelers have long favored an account of this process that takes its inspiration from the truth-preserving powers of formal deduction techniques. There is, however, an alternative hypothesis that is better able to account for the human capacity to predict the consequences worldly alterations. This alternative takes its inspiration from the powers of truth preservation exhibited by scale models and leads to a determinate computational solution to the frame problem

Bickhard, Mark H. (2000). Motivation and Emotion: An Interactive Process Model. In Ralph D. Ellis & Natika Newton (eds.), The Caldron of Consciousness: Motivation, Affect and Self-Organization. John Benjamins. (Cited by 19 | Google | More links | Edit)

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

Birnbaum, L. (1991). Rigor mortis: A response to Nilsson's 'logic and artificial intelligence'. Artificial Intelligence 47:57-78. (Cited by 106 | Google | More links | Edit)

Chalmers, David J.; French, Robert M. & Hofstadter, Douglas R. (1992). High-level perception, representation, and analogy: A critique of AI methodology. Journal of Experimental and Theoretical Artificial Intelligence. (Cited by 1 | Annotation | Google | Edit)

Chalmers, David J.; French, Robert M. & Hofstadter, Douglas R. (1992). High-level perception, representation, and analogy:A critique of artificial intelligence methodology. Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211. (Cited by 123 | Google | More links | Edit)

Abstract: High-level perceptionÃ¢ÂÂthe process of making sense of complex data at an abstract, conceptual levelÃ¢ÂÂis 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 modelsÃ¢ÂÂnotably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thoughtÃ¢ÂÂand 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 Ã¢ÂÂrepresentation moduleÃ¢ÂÂ 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

Clark, Andy (1986). A biological metaphor. Mind and Language 1:45-64. (Cited by 6 | Annotation | Google | Edit)

Clark, Andy (1987). The kludge in the machine. Mind and Language 2:277-300. (Cited by 12 | Google | Edit)

Colombetti, Giovanna (2007). Enactive appraisal. Phenomenology and the Cognitive Sciences. (Cited by 4 | Google | More links | Edit)

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

Colombetti, Giovanna & Thompson, Evan (forthcoming). The feeling body: Towards an enactive approach to emotion. In W. F. Overton, U. Mueller & J. Newman (eds.), Body in Mind, Mind in Body: Developmental Perspectives on Embodiment and Consciousness. Erlbaum. (Cited by 3 | Google | More links | Edit)

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 âappraisal processes.â 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, âheadyâ 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 cognitivismâs disembodied conception of cognition continues to shape the way emotion theorists conceptualize emotion

Dascal, M. (1992). Why does language matter to artificial intelligence? Minds and Machines 2 (2):145-174. (Cited by 7 | Google | More links | Edit)

Abstract: Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored

Dietrich, Eric (1994). AI and the tyranny of Galen, or why evolutionary psychology and cognitive ethology are important to artificial intelligence. Journal of Experimental And Theoretical Artificial Intelligence 6 (4):325-330. (Google | More links | Edit)

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)

Dreyfus, Hubert L. (1981). From micro-worlds to knowledge: AI at an impasse. In J. Haugel (ed.), Mind Design. MIT Press. (Annotation | Google | Edit)

Dreyfus, Hubert L. & Dreyfus, Stuart E. (1988). Making a mind versus modeling the brain: AI at a crossroads. Daedalus. (Cited by 6 | Annotation | Google | Edit)

Dreyfus, Hubert L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Philosophical Psychology 20 (2):247 – 268. (Cited by 2 | Google | More links | Edit)

Elster, Jon (1996). Rationality and the emotions. Economic Journal 106:1386-97. (Cited by 63 | Google | More links | Edit)

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

Flach, P. A. (ed.) (1991). Future Directions in Artificial Intelligence. New York: Elsevier Science. (Cited by 2 | Google | Edit)

Griffiths, Paul E. & Scarantino, Andrea (2005). Emotions in the Wild: The Situated Perspective on Emotion. In P. Robbins & Murat Aydede (eds.), The Cambridge Handbook of Situated Cognition. Cambridge University Press. &

6.4c	AI Methodology [32]

6.4e	Robotics [34]

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