Probabilistic bipolar abstract argumentation frameworks: complexity results

Probabilistic bipolar abstract argumentation frameworks: complexity results

Bettina Fazzinga, Sergio Flesca, Filippo Furfaro

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1803-1809. https://doi.org/10.24963/ijcai.2018/249

Probabilistic Bipolar Abstract Argumentation Frameworks (prBAFs), combining the possibility of specifying supports between arguments with a probabilistic modeling of the uncertainty, are considered, and the complexity of the fundamentalproblem of computing extensions' probabilities is addressed.The most popular semantics of supports and extensions are considered, as well as different paradigms for defining the probabilistic encoding of the uncertainty.Interestingly, the presence of supports, which does not alter the complexity of verifying extensions in the deterministic case, is shown to introduce a new source of complexity in some probabilistic settings, for which tractable cases are also identified.
Keywords:
Knowledge Representation and Reasoning: Computational Complexity of Reasoning
Knowledge Representation and Reasoning: Computational Models of Argument