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.
|Name||Frontiers in Artificial Intelligence and Applications|
|Conference||Computational Models of Argument |
|Period||4/09/20 → 11/09/20|