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  1. A truth maintenance system.Jon Doyle - 1979 - Artificial Intelligence 12 (3):231-272.
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  • A logic for default reasoning.Ray Reiter - 1980 - Artificial Intelligence 13 (1-2):81-137.
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  • Self-defeating arguments.John L. Pollock - 1991 - Minds and Machines 1 (4):367-392.
    An argument is self-defeating when it contains defeaters for some of its own defeasible lines. It is shown that the obvious rules for defeat among arguments do not handle self-defeating arguments correctly. It turns out that they constitute a pervasive phenomenon that threatens to cripple defeasible reasoning, leading to almost all defeasible reasoning being defeated by unexpected interactions with self-defeating arguments. This leads to some important changes in the general theory of defeasible reasoning.
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  • Non-monotonic logic I.Drew McDermott & Jon Doyle - 1980 - Artificial Intelligence 13 (1-2):41-72.
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  • Defeasible Reasoning.John L. Pollock - 1987 - Cognitive Science 11 (4):481-518.
    There was a long tradition in philosophy according to which good reasoning had to be deductively valid. However, that tradition began to be questioned in the 1960’s, and is now thoroughly discredited. What caused its downfall was the recognition that many familiar kinds of reasoning are not deductively valid, but clearly confer justification on their conclusions. Here are some simple examples.
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  • All I know: A study in autoepistemic logic.Hector J. Levesque - 1990 - Artificial Intelligence 42 (2-3):263-309.
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  • A general framework for reason maintenance.Drew McDermott - 1991 - Artificial Intelligence 50 (3):289-329.
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  • The Reference Class.Henry E. Kyburg - 1983 - Philosophy of Science 50 (3):374-397.
    The system presented by the author in The Logical Foundations of Statistical Inference suffered from certain technical difficulties, and from a major practical difficulty; it was hard to be sure, in discussing examples and applications, when you had got hold of the right reference class. The present paper, concerned mainly with the characterization of randomness, resolves the technical difficulties and provides a well structured framework for the choice of a reference class. The definition of randomness that leads to this framework (...)
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  • A sceptical theory of inheritance in nonmonotonic semantic networks.John F. Horty, Richmond H. Thomason & David S. Touretzky - 1990 - Artificial Intelligence 42 (2-3):311-348.
    inheritance reasoning in semantic networks allowing for multiple inheritance with exceptions. The approach leads to a definition of iaheritance that is..
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  • Trial and error predicates and the solution to a problem of Mostowski.Hilary Putnam - 1965 - Journal of Symbolic Logic 30 (1):49-57.
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  • Implication and analyticity.John L. Pollock - 1965 - Journal of Philosophy 62 (6):150-157.
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  • (1 other version)Limiting recursion.E. Mark Gold - 1965 - Journal of Symbolic Logic 30 (1):28-48.
    A class of problems is called decidable if there is an algorithm which will give the answer to any problem of the class after a finite length of time. The purpose of this paper is to discuss the classes of problems that can be solved by infinitely long decision procedures in the following sense: An algorithm is given which, for any problem of the class, generates an infinitely long sequence of guesses. The problem will be said to be solved in (...)
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  • Formalizing nonmonotonic reasoning systems.David W. Etherington - 1987 - Artificial Intelligence 31 (1):41-85.
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  • A circumscriptive theorem prover.Matthew L. Ginsberg - 1989 - Artificial Intelligence 39 (2):209-230.
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  • Criteria and our knowledge of the material world.John L. Pollock - 1967 - Philosophical Review 76 (1):28-60.
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