Skeptical Reasoning with Preferred Semantics in Abstract Argumentation without Computing Preferred Extensions

Skeptical Reasoning with Preferred Semantics in Abstract Argumentation without Computing Preferred Extensions

Matthias Thimm, Federico Cerutti, Mauro Vallati

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 2069-2075. https://doi.org/10.24963/ijcai.2021/285

We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i.e., the problem of deciding whether an argument is contained in all maximally admissible sets, a.k.a. preferred extensions. State-of-the-art algorithms solve this problem with iterative calls to an external SAT-solver to determine preferred extensions. We provide a new characterisation of skeptical acceptance wrt. preferred semantics that does not involve the notion of a preferred extension. We then develop a new algorithm that also relies on iterative calls to an external SAT-solver but avoids the costly part of maximising admissible sets. We present the results of an experimental evaluation that shows that this new approach significantly outperforms the state of the art. We also apply similar ideas to develop a new algorithm for computing the ideal extension.
Keywords:
Knowledge Representation and Reasoning: Computational Models of Argument