Proceedings

Quantitative Reasoning over Incomplete Abstract Argumentation Frameworks

Bettina Fazzinga, Sergio Flesca, Filippo Furfaro, Giuseppina Monterosso

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Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Main Track. Pages 3360-3368.

https://doi.org/10.24963/ijcai.2024/372

Keywords

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

Abstract

We introduce PERCVER and PERCACC, the problems asking for the percentages of the completions of an incomplete Abstract Argumentation Framework (iAAF) where a set of arguments S is an extension and an argument a is accepted, respectively. These problems give insights into the status of S and a more precise than the “traditional” verification and acceptance tests under the possible and necessary perspectives, that decide if S is an extension and a is accepted in at least one or every completion, respectively. As a first contribution, we investigate the relationship between the proposed framework and probabilistic AAFs (prAAFs) under the constellations approach (that, at first sight, seem to be suitable for starightforwardly encoding the quantitative reasoning underlying PERCVER and PERCACC). In this regard, we show that translating an iAAF into an equivalent prAAF requires a heavy computational cost: this backs the study of PERCVER and PERCACC as new distinguished problems. Then, we investigate the complexity of PERCVER and PERCACC, and we identify interesting islands of tractability.

Bettina Fazzinga, Sergio Flesca, Filippo Furfaro, and Giuseppina Monterosso. 2024. Quantitative Reasoning over Incomplete Abstract Argumentation Frameworks. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24. IJCAI, 3360–3368. https://doi.org/10.24963/ijcai.2024/372
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