{"type":"ScholarlyArticle","title":"Quantitative Claim-Centric Reasoning in Logic-Based Argumentation","authors":[{"name":"Markus Hecher","slug":"markus-hecher","given":"Markus","family":"Hecher","affiliation":"CSAIL , Massachusetts Institute of Technology , United States"},{"name":"Yasir Mahmood","slug":"yasir-mahmood","given":"Yasir","family":"Mahmood","affiliation":"DICE group , Department of Computer Science , Paderborn University , Germany"},{"name":"Arne Meier","slug":"arne-meier","given":"Arne","family":"Meier","affiliation":"Institut für Theoretische Informatik , Leibniz Universität Hannover , Germany"},{"name":"Johannes Schmidt","slug":"johannes-schmidt","given":"Johannes","family":"Schmidt","affiliation":"Department of Computer Science and Informatics , Jönköping University , Sweden"}],"abstract":"Argumentation is a well-established formalism for nonmonotonic reasoning, with popular frameworks being Dung’s abstract argumentation (AFs) or logic-based argumentation (Besnard-Hunter’s framework). Structurally, a set of formulas forms support for a claim if it is consistent, subset-minimal, and implies the claim. Then, an argument comprises support and a claim. We observe that the computational task (ARG) of asking for support of a claim in a knowledge base is “brave”, since many claims with a single support are accepted. As a result, ARG falls short when it comes to the question of confidence in a claim, or claim strength. In this paper, we propose a concept for measuring the (acceptance) strength of claims, based on counting supports for a claim. Further, we settle classical and structural complexity of counting arguments favoring a given claim in propositional knowledge bases (KBs). We introduce quantitative reasoning to measure the strength of claims in a KB and to determine the relevance strength of a formula for a claim.","doi":"10.24963/ijcai.2024/377","url":"https://ijcai.org/proceedings/2024/377","pdf":"https://ijcai.org/proceedings/2024/0377.pdf","markdown":"https://ijcai.org/proceedings/2024/377.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/377","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":377,"pages":{"first":3404,"last":3412},"keywords":["Knowledge Representation and Reasoning: KRR: Computational complexity of reasoning","Knowledge Representation and Reasoning: KRR: Argumentation"],"videos":[],"references":[{"position":1,"raw":"[Alfano et al., 2020] Gianvincenzo Alfano, Marco Calautti, Sergio Greco, Francesco Parisi, and Irina Trubitsyna. 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