Sets of Attacking Arguments for Inconsistent Datalog Knowledge Bases

Bruno Yun (1) , Srdjan Vesic (2) , Madalina Croitoru (3)

1 University of Aberdeen
2 CRIL - Centre de Recherche en Informatique de Lens
3 GRAPHIK - Graphs for Inferences on Knowledge

Logic-based argumentation is a well-known approach for reasoning with inconsistent logic knowledge bases. Such frameworks have been shown to suffer from a major practical drawback consisting of a large number of arguments and attacks. To address this issue, we provide an argumentation framework that considers sets of attacking arguments and provide a theoretical analysis of the new framework with respect to its syntactic and semantic properties. We provide a tool for generating such argumentation frameworks from a Datalog knowledge base and study their characteristics.

Mots clés: argumentation; datalog; SETAF

Domaines: Intelligence artificielle [cs.AI]

Bruno Yun, Srdjan Vesic, Madalina Croitoru. Sets of Attacking Arguments for Inconsistent Datalog Knowledge Bases. COMMA 2020 - 8th International Conference on Computational Models of Argument, Sep 2020, Perugia / Virtual), Italy. pp.419-430, ⟨10.3233/FAIA200526⟩. ⟨hal-03002140⟩