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  1. A logic for default reasoning.Ray Reiter - 1980 - Artificial Intelligence 13 (1-2):81-137.
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  • Non-monotonic logic I.Drew McDermott & Jon Doyle - 1980 - Artificial Intelligence 13 (1-2):41-72.
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  • A mathematical treatment of defeasible reasoning and its implementation.Guillermo R. Simari & Ronald P. Loui - 1992 - Artificial Intelligence 53 (2-3):125-157.
    We present a mathematical approach to defeasible reasoning based on arguments. This approach integrates the notion of specificity introduced by Poole and the theory of warrant presented by Pollock. The main contribution of this paper is a precise, well-defined system which exhibits correct behavior when applied to the benchmark examples in the literature. It aims for usability rather than novelty. We prove that an order relation can be introduced among equivalence classes of arguments under the equi-specificity relation. We also prove (...)
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  • Preferred answer sets for extended logic programs.Gerhard Brewka & Thomas Eiter - 1999 - Artificial Intelligence 109 (1-2):297-356.
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  • Applications of Circumscription to Formalizing Common Sense Knowledge.John McCarthy - 1986 - Artificial Intelligence 28 (1):89–116.
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  • Circumscription — A Form of Non-Monotonic Reasoning.John McCarthy - 1980 - Artificial Intelligence 13 (1-2):27–39.
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  • Semantic Considerations on nonmonotonic Logic.Robert C. Moore - 1985 - Artificial Intelligence 25 (1):75-94.
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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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  • On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.Phan Minh Dung - 1995 - Artificial Intelligence 77 (2):321-357.
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  • Nonmonotonic reasoning, preferential models and cumulative logics.Sarit Kraus, Daniel Lehmann & Menachem Magidor - 1990 - Artificial Intelligence 44 (1-2):167-207.
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  • On Stein's paper: resolving ambiguity in nonmonotonic inheritance hierarchies.Geneviève Simonet & Roland Ducournau - 1994 - Artificial Intelligence 71 (1):183-193.
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  • Compiling defeasible inheritance networks to general logic programs.Jia-Huai You, Xianchang Wang & Li Yan Yuan - 1999 - Artificial Intelligence 113 (1-2):247-268.
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  • Qualitative probabilities for default reasoning, belief revision, and causal modeling.Moisés Goldszmidt & Judea Pearl - 1996 - Artificial Intelligence 84 (1-2):57-112.
    This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...)
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  • Resolving ambiguity in nonmonotonic inheritance hierarchies.Lynn Andrea Stein - 1992 - Artificial Intelligence 55 (2-3):259-310.
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  • On Sandewall's paper: Nonmonotonic inference rules for multiple inheritance with exceptions.Geneviève Simonet - 1996 - Artificial Intelligence 86 (2):359-374.
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  • Cumulative default logic.Gerhard Brewka - 1991 - Artificial Intelligence 50 (2):183-205.
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  • Compiling specificity into approaches to nonmonotonic reasoning.James P. Delgrande & Torsten H. Schaub - 1997 - Artificial Intelligence 90 (1-2):301-348.
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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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  • An abstract, argumentation-theoretic approach to default reasoning.A. Bondarenko, P. M. Dung, R. A. Kowalski & F. Toni - 1997 - Artificial Intelligence 93 (1-2):63-101.
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  • The complexity of path-based defeasible inheritance.Bart Selman & Hector J. Levesque - 1993 - Artificial Intelligence 62 (2):303-339.
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  • Conditional entailment: Bridging two approaches to default reasoning.Hector Geffner & Judea Pearl - 1992 - Artificial Intelligence 53 (2-3):209-244.
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