Redefining ABA+ Semantics via Abstract Set-to-Set Attacks

Abstract

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.

Cite

Text

Dimopoulos et al. "Redefining ABA+ Semantics via Abstract Set-to-Set Attacks." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I9.28918

Markdown

[Dimopoulos et al. "Redefining ABA+ Semantics via Abstract Set-to-Set Attacks." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/dimopoulos2024aaai-redefining/) doi:10.1609/AAAI.V38I9.28918

BibTeX

@inproceedings{dimopoulos2024aaai-redefining,
  title     = {{Redefining ABA+ Semantics via Abstract Set-to-Set Attacks}},
  author    = {Dimopoulos, Yannis and Dvorák, Wolfgang and König, Matthias and Rapberger, Anna and Ulbricht, Markus and Woltran, Stefan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {10493-10500},
  doi       = {10.1609/AAAI.V38I9.28918},
  url       = {https://mlanthology.org/aaai/2024/dimopoulos2024aaai-redefining/}
}