Efficiently computing extensions' probabilities over probabilistic Bipolar Abstract Argumentation Frameworks

Probabilistic Bipolar Abstract Argumentation Frameworks (prBAFs), combining the possibility of specifying supports between arguments with a probabilistic modeling of the uncertainty, have been recently considered [34, 35] and the complexity of the problem of computing extensions' probabilities has been characterized [22]. In this paper we deal with the problem of computing extensions' probabilities over prBAFs where the probabilistic events that arguments, supports and defeats occur in the real scenario are assumed to be independent probabilistic events (prBAFS of type IND). Specifically an algorithm for efficiently computing extensions' probabilities under the stable and admissible semantics has been devised and its efficiency has been experimentally validated w.r.t. the exhaustive approach, i.e. the approach consisting in the generation of all the possible scenarios, showing that the proposed algorithm outperforms the exhaustive approach.