Redefining ABA+ Semantics via Abstract Set-to-Set Attacks
DOI:
https://doi.org/10.1609/aaai.v38i9.28918Keywords:
KRR: Argumentation, KRR: Computational Complexity of Reasoning, KRR: Nonmonotonic ReasoningAbstract
Assumption-based argumentation (ABA) is a powerful defeasible reasoning formalism which is based on the interplay of assumptions, their contraries, and inference rules. ABA with preferences (ABA+) generalizes the basic model by allowing qualitative comparison between assumptions. The integration of preferences however comes with a cost. In ABA+, the evaluation under two central and well-established semantics---grounded and complete semantics---is not guaranteed to yield an outcome. Moreover, while ABA frameworks without preferences allow for a graph-based representation in Dung-style frameworks, an according instantiation for general ABA+ frameworks has not been established so far. In this work, we tackle both issues: First, we develop a novel abstract argumentation formalism based on set-to-set attacks. We show that our so-called Hyper Argumentation Frameworks (HYPAFs) capture ABA+. Second, we propose relaxed variants of complete and grounded semantics for HYPAFs that yield an extension for all frameworks by design, while still faithfully generalizing the established semantics of Dung-style Argumentation Frameworks. We exploit the newly established correspondence between ABA+ and HYPAFs to obtain variants for grounded and complete ABA+ semantics that are guaranteed to yield an outcome. Finally, we discuss basic properties and provide a complexity analysis. Along the way, we settle the computational complexity of several ABA+ semantics.Downloads
Published
2024-03-24
How to Cite
Dimopoulos , Y., Dvorak, W., König, M., Rapberger, A., Ulbricht, M., & Woltran, S. (2024). Redefining ABA+ Semantics via Abstract Set-to-Set Attacks. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 10493–10500. https://doi.org/10.1609/aaai.v38i9.28918
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Section
AAAI Technical Track on Knowledge Representation and Reasoning