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  1. A psychologically plausible logical model of conceptualization.Hong-Gee Kim - 1997 - Minds and Machines 7 (2):249-267.
    This paper discusses how we understand and use a concept or the meaningof a general term to identify the objects falling under the term. There aretwo distinct approaches to research on the problems of concepts and meaningthe psychological approach and the formal (or logical) approach. My majorconcern is to consider the possibility of reconciling these two differentapproaches, and for this I propose to build a psychologically plausibleformal system of conceptualization. That is, I will develop a theory-basedaccount of concepts and propose (...)
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  • Between constructive mathematics and PROLOG.Gerhard Jäger - 1991 - Archive for Mathematical Logic 30 (5-6):297-310.
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  • On the relationship between circumscription and negation as failure.Michael Gelfond, Halina Przymusinska & Teodor Przymusinski - 1989 - Artificial Intelligence 38 (1):75-94.
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  • (1 other version)Logic programming and knowledge representation—The A-Prolog perspective.Michael Gelfond & Nicola Leone - 2002 - Artificial Intelligence 138 (1-2):3-38.
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  • Sound and efficient closed-world reasoning for planning.Oren Etzioni, Keith Golden & Daniel S. Weld - 1997 - Artificial Intelligence 89 (1-2):113-148.
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  • On the complexity of propositional knowledge base revision, updates, and counterfactuals.Thomas Eiter & Georg Gottlob - 1992 - Artificial Intelligence 57 (2-3):227-270.
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  • Expressing preferences in default logic.James P. Delgrande & Torsten Schaub - 2000 - Artificial Intelligence 123 (1-2):41-87.
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  • Is intractability of nonmonotonic reasoning a real drawback?Marco Cadoli, Francesco M. Donini & Marco Schaerf - 1996 - Artificial Intelligence 88 (1-2):215-251.
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  • An efficient method for eliminating varying predicates from a circumscription.Marco Cadoli, Thomas Eiter & Georg Gottlob - 1992 - Artificial Intelligence 54 (3):397-410.
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  • An algorithm to compute circumscription.Teodor C. Przymusinski - 1989 - Artificial Intelligence 38 (1):49-73.
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  • What should default reasoning be, by default?Jeff Pelletier - unknown
    This is a position paper concerning the role of empirical studies of human default reasoning in the formalization of AI theories of default reasoning. We note that AI motivates its theoretical enterprise by reference to human skill at default reasoning, but that the actual research does not make any use of this sort of information and instead relies on intuitions of individual investigators. We discuss two reasons theorists might not consider human performance relevant to formalizing default reasoning: (a) that intuitions (...)
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