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  1. Is default logic a reinvention of inductive-statistical reasoning?Yao-Hua Tan - 1997 - Synthese 110 (3):357-379.
    Currently there is hardly any connection between philosophy of science and Artificial Intelligence research. We argue that both fields can benefit from each other. As an example of this mutual benefit we discuss the relation between Inductive-Statistical Reasoning and Default Logic. One of the main topics in AI research is the study of common-sense reasoning with incomplete information. Default logic is especially developed to formalise this type of reasoning. We show that there is a striking resemblance between inductive-statistical reasoning and (...)
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  • Resolving ambiguity in nonmonotonic inheritance hierarchies.Lynn Andrea Stein - 1992 - Artificial Intelligence 55 (2-3):259-310.
  • Modal logic based theory for non-monotonic reasoning.Pierre Siegel & Camilla Schwind - 1993 - Journal of Applied Non-Classical Logics 3 (1):73-92.
    ABSTRACT This paper defines a new modal logic based theory for non-monotonic reasoning. This logic expresses notions about hypotheses and known information. These notions are defined in the framework of the modal system τ. A translation of default logic in terms of hypothesis theory is given with which it is possible to fully characterize default logic by giving a necessary and sufficient criterion for the existence and the non-existence of extensions. Moreover several problems relating to non-monotonic reasoning are discussed (and (...)
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  • Prioritized logic programming and its application to commonsense reasoning.Chiaki Sakama & Katsumi Inoue - 2000 - Artificial Intelligence 123 (1-2):185-222.
  • A logical framework for default reasoning.David Poole - 1988 - Artificial Intelligence 36 (1):27-47.
  • The theory of nomic probability.John L. Pollock - 1992 - Synthese 90 (2):263 - 299.
    This article sketches a theory of objective probability focusing on nomic probability, which is supposed to be the kind of probability figuring in statistical laws of nature. The theory is based upon a strengthened probability calculus and some epistemological principles that formulate a precise version of the statistical syllogism. It is shown that from this rather minimal basis it is possible to derive theorems comprising (1) a theory of direct inference, and (2) a theory of induction. The theory of induction (...)
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  • How to reason defeasibly.John L. Pollock - 1992 - Artificial Intelligence 57 (1):1-42.
  • Default theories that always have extensions.Christos H. Papadimitriou & Martha Sideri - 1994 - Artificial Intelligence 69 (1-2):347-357.
  • The anomalous extension problem in default reasoning.Paul H. Morris - 1988 - Artificial Intelligence 35 (3):383-399.
  • Alternative foundations for Reiter's default logic.Thomas Linke & Torsten Schaub - 2000 - Artificial Intelligence 124 (1):31-86.
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  • A two-level system of knowledge representation based on evidential probability.Henry E. Kyburg - 1991 - Philosophical Studies 64 (1):105 - 114.
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  • From the textual description of an accident to its causes.Daniel Kayser & Farid Nouioua - 2009 - Artificial Intelligence 173 (12-13):1154-1193.
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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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  • Argument construction and reinstatement in logics for defeasible reasoning.John F. Horty - 2001 - Artificial Intelligence and Law 9 (1):1-28.
    This paper points out some problems with two recent logical systems – one due to Prakken and Sartor, the other due to Kowalski and Toni – designedfor the representation of defeasible arguments in general, but with a specialemphasis on legal reasoning.
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  • Nonmonotonic logic and temporal projection.Steve Hanks & Drew McDermott - 1987 - Artificial Intelligence 33 (3):379-412.
  • Formalizing nonmonotonic reasoning systems.David W. Etherington - 1987 - Artificial Intelligence 31 (1):41-85.
  • Nonmonotonicity and the scope of reasoning.David W. Etherington, Sarit Kraus & Donald Perlis - 1991 - Artificial Intelligence 52 (3):221-261.
  • Alternative approaches to default logic.James P. Delgrande, Torsten Schaub & W. Ken Jackson - 1994 - Artificial Intelligence 70 (1-2):167-237.
  • Learning to plan in continuous domains.Gerald F. DeJong - 1994 - Artificial Intelligence 65 (1):71-141.
  • Artificial nonmonotonic neural networks.B. Boutsinas & M. N. Vrahatis - 2001 - Artificial Intelligence 132 (1):1-38.
  • Yet some more complexity results for default logic.Rachel Ben-Eliyahu-Zohary - 2002 - Artificial Intelligence 139 (1):1-20.
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  • Default reasoning using classical logic.Rachel Ben-Eliyahu & Rina Dechter - 1996 - Artificial Intelligence 84 (1-2):113-150.
  • Defeasible inheritance on cyclic networks.Gian Aldo Antonelli - 1997 - Artificial Intelligence 92 (1-2):1-23.
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  • Outlier detection using default reasoning.Fabrizio Angiulli, Rachel Ben-Eliyahu – Zohary & Luigi Palopoli - 2008 - Artificial Intelligence 172 (16-17):1837-1872.
  • Defaults as restrictions on classical Hilbert-style proofs.Gianni Amati, Luigia Carlucci Aiello & Fiora Pirri - 1994 - Journal of Logic, Language and Information 3 (4):303-326.
    Since the earliest formalisation of default logic by Reiter many contributions to this appealing approach to nonmonotonic reasoning have been given. The different formalisations are here presented in a general framework that gathers the basic notions, concepts and constructions underlying default logic. Our view is to interpret defaults as special rules that impose a restriction on the juxtaposition of monotonic Hubert-style proofs of a given logicL. We propose to describe default logic as a logic where the juxtaposition of default proofs (...)
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  • Philosophy and Cognitive Sciences: Proceedings of the 16th International Wittgenstein Symposium (Kirchberg Am Wechsel, Austria 1993).Roberto Casati & Barry Smith (eds.) - 1994 - Vienna: Wien: Hölder-Pichler-Tempsky.
    Online collection of papers by Devitt, Dretske, Guarino, Hochberg, Jackson, Petitot, Searle, Tye, Varzi and other leading thinkers on philosophy and the foundations of cognitive Science. Topics dealt with include: Wittgenstein and Cognitive Science, Content and Object, Logic and Foundations, Language and Linguistics, and Ontology and Mereology.
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