Monotonic and Nonmonotonic Inference for Abstract Argumentation

Richard Booth 1, 2 Souhila Kaci 3 Tjitze Rienstra 4 Leon van der Torre 4
1 SnT - Interdisciplinary Centre for Security, Reliability and Trust [Luxembourg]
2 FSTC - Faculty of Science, Technology and Communication [Luxembourg]
3 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
4 ICR - Individual and Collective Reasoning
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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Conference papers
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00857793
Contributor : Souhila Kaci <>
Submitted on : Wednesday, September 4, 2013 - 9:46:38 AM
Last modification on : Friday, November 8, 2019 - 3:06:02 PM

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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. FLAIRS: Florida Artificial Intelligence Research Society, May 2013, St. Pete Beach, Florida, United States. pp.1-8. ⟨lirmm-00857793⟩

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