Proceedings

Robustness in Single-Audience Value-based Abstract Argumentation: Complexity Results

Bettina Fazzinga, Sergio Flesca, Filippo Furfaro

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4482-4490.

https://doi.org/10.24963/ijcai.2025/499

Keywords

  • Knowledge Representation and Reasoning: KRR: Computational complexity of reasoning
  • Knowledge Representation and Reasoning: KRR: Argumentation

Abstract

We address the context of Single-Audience Value-Based Abstract Argumentation Framework (AVAF),

where the arguments are labeled with the social values that they promote and

the activation/deactivation of the attacks depends on the audience profile

(expressed as a set of preferences between the social values).

Herein, we introduce a new notion of robustness for measuring the sensitivity of the outcome of the reasoning to the extent of changes in the audience profile.

In particular, for a set of arguments S or a single argument a, we define the robustness degree

of the status of S or a as the maximum number k* of deletions/insertions of preferences

from/into the audience profile that are tolerable, in the sense that S remains an extension

(or a non-extension) or a accepted (or unaccepted) after performing at most k* deletions/insertions.

We introduce the decision problems related to the computation of the robustness degree and focus on thoroughly investigating their computational complexity.

Bettina Fazzinga, Sergio Flesca, and Filippo Furfaro. 2025. Robustness in Single-Audience Value-based Abstract Argumentation: Complexity Results. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4482–4490. https://doi.org/10.24963/ijcai.2025/499
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