MindPapers: 6.2e. Computation and Representation, Misc

Akman, Varol & ten Hagen, Paul J. W. (1989). The power of physical representations. AI Magazine 10 (3):49-65. (Cited by 10 | Google | More links | Edit)

Bailey, Andrew R. (1994). Representations versus regularities: Does computation require representation? Eidos 12 (1):47-58. (Google | Edit)

Dartnall, Terry (2000). Reverse psychologism, cognition and content. Minds and Machines 10 (1):31-52. (Cited by 32 | Google | More links | Edit)

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

Dietrich, Eric (1988). Computers, intentionality, and the new dualism. Computers and Philosophy Newsletter. (Google | Edit)

Dreyfus, Hubert L. (1979). A framework for misrepresenting knowledge. In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence. Humanities Press. (Cited by 7 | Annotation | Google | Edit)

Fields, Christopher A. (1994). Real machines and virtual intentionality: An experimentalist takes on the problem of representational content. In Eric Dietrich (ed.), Thinking Computers and Virtual Persons. Academic Press. (Google | Edit)

Guvenir, Halil A. & Akman, Varol (1992). Problem representation for refinement. Minds and Machines 2 (3):267-282. (Google | More links | Edit)

Abstract: In this paper we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples

Haugeland, John (1981). Semantic engines: An introduction to mind design. In J. Haugel (ed.), Mind Design. MIT Press. (Cited by 92 | Google | Edit)

Prem, Erich (2000). Changes of representational AI concepts induced by embodied autonomy. Communication and Cognition-Artificial Intelligence 17 (3-4):189-208. (Cited by 4 | Google | Edit)

Robinson, William S. (1995). Direct representation. Philosophical Studies 80 (3):305-22. (Cited by 3 | Annotation | Google | More links | Edit)

Shani, Itay (2005). Computation and intentionality: A recipe for epistemic impasse. Minds and Machines 15 (2):207-228. (Cited by 1 | Google | More links | Edit)

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

Stanley, Jason (2005). Review of Robyn Carston, Thoughts and Utterances. Mind and Language 20 (3). (Google | Edit)

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

Thornton, Chris (1997). Brave mobots use representation: Emergence of representation in fight-or-flight learning. Minds and Machines 7 (4):475-494. (Cited by 10 | Google | More links | Edit)

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

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