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.
Document type :
Conference papers
Complete list of metadatas

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

Identifiers

  • HAL Id : lirmm-00857793, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

641