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Sets of Attacking Arguments for Inconsistent Datalog Knowledge Bases
Bruno Yun, Srdjan Vesic, Madalina Croitoru
419 - 430
10.3233/FAIA200526
Research Article
Frontiers in Artificial Intelligence and Applications
Volume 326: Computational Models of Argument
Abstract

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.

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