David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Synthese 187 (S1):43-72 (2012)
As many philosophers agree, the frame problem is concerned with how an agent may efficiently filter out irrelevant information in the process of problem-solving. Hence, how to solve this problem hinges on how to properly handle semantic relevance in cognitive modeling, which is an area of cognitive science that deals with simulating human's cognitive processes in a computerized model. By "semantic relevance", we mean certain inferential relations among acquired beliefs which may facilitate information retrieval and practical reasoning under certain epistemic constraints, e. g., the insufficiency of knowledge, the limitation of time budget, etc. However, traditional approaches to relevance—as for example, relevance logic, the Bayesian approach, as well as Description Logic—have failed to do justice to the foregoing constraints, and in this sense, they are not proper tools for solving the frame problem/relevance problem. As we will argue in this paper, Non-Axiomatic Reasoning System (NARS) can handle the frame problem in a more proper manner, because the resulting solution seriously takes epistemic constraints on cognition as a fundamental theoretical principle
|Keywords||The frame problem Semantic relevance Confirmation paradox The Bayesian approach Description Logic Non-Axiomatic Reasoning System|
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Judea Pearl (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
Jerry A. Fodor (2000). The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology. MIT Press.
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Willard V. O. Quine (1951). Two Dogmas of Empiricism. Philosophical Review 60 (1):20–43.
Judea Pearl (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
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