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Conference papers

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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Contributor : Souhila Kaci Connect in order to contact the contributor
Submitted on : Wednesday, September 4, 2013 - 9:46:38 AM
Last modification on : Friday, August 5, 2022 - 3:03:00 PM


  • HAL Id : lirmm-00857793, version 1



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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