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  1. Perceiving and reasoning about a changing world.John Pollock - unknown
    A rational agent (artificial or otherwise) residing in a complex changing environment must gather information perceptually, update that information as the world changes, and combine that information with causal information to reason about the changing world. Using the system of defeasible reasoning that is incorporated into the OSCAR architecture for rational agents, a set of reasonschemas is proposed for enabling an agent to perform some of the requisite reasoning. Along the way, solutions are proposed for the Frame Problem, the Qualification (...)
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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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  • Abstract argumentation systems.Gerard A. W. Vreeswijk - 1997 - Artificial Intelligence 90 (1-2):225-279.
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  • (1 other version)Skepticism and floating conclusions.John F. Horty - 2002 - Artificial Intelligence 135 (1-2):55-72.
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  • ``Defeasible Reasoning with Variable Degrees of Justification".John L. Pollock - 2001 - Artificial Intelligence 133 (1-2):233-282.
    The question addressed in this paper is how the degree of justification of a belief is determined. A conclusion may be supported by several different arguments, the arguments typically being defeasible, and there may also be arguments of varying strengths for defeaters for some of the supporting arguments. What is sought is a way of computing the “on sum” degree of justification of a conclusion in terms of the degrees of justification of all relevant premises and the strengths of all (...)
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  • A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication.Y. Xiang - 1996 - Artificial Intelligence 87 (1-2):295-342.
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  • Floating conclusions and zombie paths: Two deep difficulties in the “directly skeptical” approach to defeasible inheritance nets.David Makinson & Karl Schlechta - 1991 - Artificial Intelligence 48 (2):199-209.
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  • Extending abstract argumentation systems theory.P. Baroni, M. Giacomin & G. Guida - 2000 - Artificial Intelligence 120 (2):251-270.
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  • (1 other version)Relating protocols for dynamic dispute with logics for defeasible argumentation.Henry Prakken - 2001 - Synthese 127 (1-2):187-219.
    This article investigates to what extent protocols for dynamicdisputes, i.e., disputes in which the information base can vary at differentstages, can be justified in terms of logics for defeasible argumentation. Firsta general framework is formulated for dialectical proof theories for suchlogics. Then this framework is adapted to serve as a framework for protocols fordynamic disputes, after which soundness and fairness properties are formulated for such protocols relative to dialectical proof theories. It then turns out that certaintypes of protocols that are (...)
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  • (1 other version)Relating Protocols For Dynamic Dispute With Logics For Defeasible Argumentation.Henry Prakken - 2001 - Synthese 127 (1-2):187-219.
    This article investigates to what extent protocols for dynamicdisputes, i.e., disputes in which the information base can vary at differentstages, can be justified in terms of logics for defeasible argumentation. Firsta general framework is formulated for dialectical proof theories for suchlogics. Then this framework is adapted to serve as a framework for protocols fordynamic disputes, after which soundness and fairness properties are formulated for such protocols relative to dialectical proof theories. It then turns out that certaintypes of protocols that are (...)
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  • Argument-based extended logic programming with defeasible priorities.Henry Prakken & Giovanni Sartor - 1997 - Journal of Applied Non-Classical Logics 7 (1-2):25-75.
    ABSTRACT Inspired by legal reasoning, this paper presents a semantics and proof theory of a system for defeasible argumentation. Arguments are expressed in a logic-programming language with both weak and strong negation, conflicts between arguments are decided with the help of priorities on the rules. An important feature of the system is that these priorities are not fixed, but are themselves defeasibly derived as conclusions within the system. Thus debates on the choice between conflicting arguments can also be modelled. The (...)
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  • Justification and defeat.John L. Pollock - 1994 - Artificial Intelligence 67 (2):377-407.
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  • How to reason defeasibly.John L. Pollock - 1992 - Artificial Intelligence 57 (1):1-42.
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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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  • Reaching agreements through argumentation: a logical model and implementation.Sarit Kraus, Katia Sycara & Amir Evenchik - 1998 - Artificial Intelligence 104 (1-2):1-69.
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  • Two party immediate response disputes: Properties and efficiency.Paul E. Dunne & T. J. M. Bench-Capon - 2003 - Artificial Intelligence 149 (2):221-250.
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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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  • Coherence in finite argument systems.Paul E. Dunne & T. J. M. Bench-Capon - 2002 - Artificial Intelligence 141 (1-2):187-203.
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  • A method for the computational modelling of dialectical argument with dialogue games.T. J. M. Bench-Capon, T. Geldard & P. H. Leng - 2000 - Artificial Intelligence and Law 8 (2-3):233-254.
    In this paper we describe a method for the specification of computationalmodels of argument using dialogue games. The method, which consists ofsupplying a set of semantic definitions for the performatives making upthe game, together with a state transition diagram, is described in full.Its use is illustrated by some examples of varying complexity, includingtwo complete specifications of particular dialogue games, Mackenzie's DC,and the authors' own TDG. The latter is also illustrated by a fully workedexample illustrating all the features of the game.
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