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  1. Logical Models of Argument.Ronald Prescott Loui, Carlos Ivan Ches~Nevar & Ana Gabriela Maguitman - 2000 - ACM Computing Surveys 32 (4):337-383.
    Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas which characterize di erent logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation. We trace the evolution of argumentationfrom the mid-80's, when argumentsystems emerged as an alternative to nonmonotonic formalisms based on classical logic, to the present, as argument is embedded in di erent complex systems for real-world applications, and allows more (...)
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  • Probabilistic rule-based argumentation for norm-governed learning agents.Régis Riveret, Antonino Rotolo & Giovanni Sartor - 2012 - Artificial Intelligence and Law 20 (4):383-420.
    This paper proposes an approach to investigate norm-governed learning agents which combines a logic-based formalism with an equation-based counterpart. This dual formalism enables us to describe the reasoning of such agents and their interactions using argumentation, and, at the same time, to capture systemic features using equations. The approach is applied to norm emergence and internalisation in systems of learning agents. The logical formalism is rooted into a probabilistic defeasible logic instantiating Dung’s argumentation framework. Rules of this logic are attached (...)
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  • Behavioral Experiments for Assessing the Abstract Argumentation Semantics of Reinstatement.Iyad Rahwan, Mohammed I. Madakkatel, Jean-François Bonnefon, Ruqiyabi N. Awan & Sherief Abdallah - 2010 - Cognitive Science 34 (8):1483-1502.
    Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these (...)
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  • Reasoning about preferences in argumentation frameworks.Sanjay Modgil - 2009 - Artificial Intelligence 173 (9-10):901-934.
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  • (2 other versions)The Stable Model Semantics for Logic Programming.Melvin Fitting - 1992 - Journal of Symbolic Logic 57 (1):274-277.
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  • Fibring Argumentation Frames.Dov M. Gabbay - 2009 - Studia Logica 93 (2):231-295.
    This paper is part of a research program centered around argumentation networks and offering several research directions for argumentation networks, with a view of using such networks for integrating logics and network reasoning. In Section 1 we introduce our program manifesto. In Section 2 we motivate and show how to substitute one argumentation network as a node in another argumentation network. Substitution is a purely logical operation and doing it for networks, besides developing their theory further, also helps us see (...)
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  • Logical Modes of Attack in Argumentation Networks.Dov M. Gabbay & Artur S. D’Avila Garcez - 2009 - Studia Logica 93 (2):199-230.
    This paper studies methodologically robust options for giving logical contents to nodes in abstract argumentation networks. It defines a variety of notions of attack in terms of the logical contents of the nodes in a network. General properties of logics are refined both in the object level and in the metalevel to suit the needs of the application. The network-based system improves upon some of the attempts in the literature to define attacks in terms of defeasible proofs, the so-called rule-based (...)
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  • Modular argumentation for modelling legal doctrines in common law of contract.Phan Minh Dung & Phan Minh Thang - 2009 - Artificial Intelligence and Law 17 (3):167-182.
    To create a programming environment for contract dispute resolution, we propose an extension of assumption-based argumentation into modular assumption-based argumentation in which different modules of argumentation representing different knowledge bases for reasoning about beliefs and facts and for representation and reasoning with the legal doctrines could be built and assembled together. A distinct novel feature of modular argumentation in compare with other modular logic-based systems like Prolog is that it allows references to different semantics in the same module at the (...)
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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.Robert A. Kowalski & Francesca Toni - 1996 - Artificial Intelligence and Law 4 (3-4):275-296.
    In this paper we explore the thesis that the role of argumentation in practical reasoning in general and legal reasoning in particular is to justify the use of defeasible rules to derive a conclusion in preference to the use of other defeasible rules to derive a conflicting conclusion. The defeasibility of rules is expressed by means of non-provability claims as additional conditions of the rules.We outline an abstract approach to defeasible reasoning and argumentation which includes many existing formalisms, including default (...)
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  • On the evaluation of argumentation formalisms.Martin Caminada & Leila Amgoud - 2007 - Artificial Intelligence 171 (5-6):286-310.
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  • How to reason defeasibly.John L. Pollock - 1992 - Artificial Intelligence 57 (1):1-42.
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  • Argument based machine learning.Martin Možina, Jure Žabkar & Ivan Bratko - 2007 - Artificial Intelligence 171 (10-15):922-937.
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