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  1. Foundations of a functional approach to knowledge representation.Hector J. Levesque - 1984 - Artificial Intelligence 23 (2):155-212.
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  • Connectionist learning procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
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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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  • Pac-learning non-recursive prolog clauses.William W. Cohen - 1995 - Artificial Intelligence 79 (1):1-38.
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  • Horn approximations of empirical data.Henry Kautz, Michael Kearns & Bart Selman - 1995 - Artificial Intelligence 74 (1):129-145.
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  • Reasoning with models.Roni Khardon & Dan Roth - 1996 - Artificial Intelligence 87 (1-2):187-213.
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  • Belief revision: A critique. [REVIEW]Nir Friedman & Joseph Y. Halpern - 1999 - Journal of Logic, Language and Information 8 (4):401-420.
    We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change carefully, we must be quite explicit about the ontology or scenario underlying the belief change process. This is something that has been missing in previous work, with its focus on postulates. Our analysis shows that we must pay particular attention to two issues that have often been taken for granted: the (...)
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  • (1 other version)On the logic of theory change: Partial meet contraction and revision functions.Carlos E. Alchourrón, Peter Gärdenfors & David Makinson - 1985 - Journal of Symbolic Logic 50 (2):510-530.
    This paper extends earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising a theory to introduce a proposition. In the course of the earlier work, Gardenfors developed general postulates of a more or less equational nature for such processes, whilst Alchourron and Makinson studied the particular case of contraction functions that are maximal, in the sense of yielding a maximal subset of the theory (or alternatively, of one of (...)
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  • Quantifying inductive bias: AI learning algorithms and Valiant's learning framework.David Haussler - 1988 - Artificial Intelligence 36 (2):177-221.
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  • Two theses of knowledge representation: Language restrictions, taxonomic classification, and the utility of representation services.Jon Doyle & Ramesh S. Patil - 1991 - Artificial Intelligence 48 (3):261-297.
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  • Structure identification in relational data.Rina Dechter & Judea Pearl - 1992 - Artificial Intelligence 58 (1-3):237-270.
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  • The refinement of probabilistic rule sets: Sociopathic interactions.David C. Wilkins & Yong Ma - 1994 - Artificial Intelligence 70 (1-2):1-32.
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  • Knowing what doesn't matter: exploiting the omission of irrelevant data.Russell Greiner, Adam J. Grove & Alexander Kogan - 1997 - Artificial Intelligence 97 (1-2):345-380.
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  • Theory refinement combining analytical and empirical methods.Dirk Ourston & Raymond J. Mooney - 1994 - Artificial Intelligence 66 (2):273-309.
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