Rediscovering Argumentation Principles Utilizing Collective Attacks
This work addresses challenges in formal AI argumentation by extending principles to SETAFs, offering incremental improvements for researchers in computational argumentation.
The authors extended the principle-based approach to Argumentation Frameworks with Collective Attacks (SETAFs) to analyze semantics, introducing concepts like the reduct and modularization principle, and demonstrated applications in incremental computation and a novel parameterized tractability result for verifying preferred extensions.
Argumentation Frameworks (AFs) are a key formalism in AI research. Their semantics have been investigated in terms of principles, which define characteristic properties in order to deliver guidance for analysing established and developing new semantics. Because of the simple structure of AFs, many desired properties hold almost trivially, at the same time hiding interesting concepts behind syntactic notions. We extend the principle-based approach to Argumentation Frameworks with Collective Attacks (SETAFs) and provide a comprehensive overview of common principles for their semantics. Our analysis shows that investigating principles based on decomposing the given SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in comparison to usual AFs. We introduce the notion of the reduct as well as the modularization principle for SETAFs which will prove beneficial for this kind of investigation. We then demonstrate how our findings can be utilized for incremental computation of extensions and give a novel parameterized tractability result for verifying preferred extensions.