Monotonic and Nonmonotonic Inference for Abstract Argumentation

Abstract : We present a new approach to reasoning about the outcome of an argumentation framework, where an agent's reasoning with a framework and semantics is represented by an inference relation defined over a logical labeling language. We first study a monotonic type of inference which is, in a sense, more general than an acceptance function, but equally expressive. In order to overcome the limitations of this expressiveness, we study a non-monotonic type of inference which allows \emph{counterfactual} inferences. We precisely characterize the classes of frameworks distinguishable by the non-monotonic inference relation for the admissible semantics.
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Communication dans un congrès
AAAI Press. FLAIRS: Florida Artificial Intelligence Research Society, May 2013, St. Pete Beach, Florida, United States. 26th International Florida Artificial Intelligence Research Society Conference, pp.1-8, 2013, FAIRS-26
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00857793
Contributeur : Souhila Kaci <>
Soumis le : mercredi 4 septembre 2013 - 09:46:38
Dernière modification le : samedi 27 janvier 2018 - 01:32:13

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  • HAL Id : lirmm-00857793, version 1

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Richard Booth, Souhila Kaci, Tjitze Rienstra, Leon Van Der Torre. Monotonic and Nonmonotonic Inference for Abstract Argumentation. AAAI Press. FLAIRS: Florida Artificial Intelligence Research Society, May 2013, St. Pete Beach, Florida, United States. 26th International Florida Artificial Intelligence Research Society Conference, pp.1-8, 2013, FAIRS-26. 〈lirmm-00857793〉

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