{"year":"2024","citation":{"mla":"Hecher, Markus, et al. “Quantitative Claim-Centric Reasoning in Logic-Based Argumentation.” Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2024, doi:10.24963/ijcai.2024/377.","bibtex":"@inproceedings{Hecher_Mahmood_Meier_Schmidt_2024, title={Quantitative Claim-Centric Reasoning in Logic-Based Argumentation}, DOI={10.24963/ijcai.2024/377}, booktitle={Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence}, publisher={International Joint Conferences on Artificial Intelligence Organization}, author={Hecher, Markus and Mahmood, Yasir and Meier, Arne and Schmidt, Johannes}, year={2024} }","short":"M. Hecher, Y. Mahmood, A. Meier, J. Schmidt, in: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, 2024.","apa":"Hecher, M., Mahmood, Y., Meier, A., & Schmidt, J. (2024). Quantitative Claim-Centric Reasoning in Logic-Based Argumentation. Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/377","chicago":"Hecher, Markus, Yasir Mahmood, Arne Meier, and Johannes Schmidt. “Quantitative Claim-Centric Reasoning in Logic-Based Argumentation.” In Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2024. https://doi.org/10.24963/ijcai.2024/377.","ieee":"M. Hecher, Y. Mahmood, A. Meier, and J. Schmidt, “Quantitative Claim-Centric Reasoning in Logic-Based Argumentation,” 2024, doi: 10.24963/ijcai.2024/377.","ama":"Hecher M, Mahmood Y, Meier A, Schmidt J. Quantitative Claim-Centric Reasoning in Logic-Based Argumentation. In: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization; 2024. doi:10.24963/ijcai.2024/377"},"publication_status":"published","title":"Quantitative Claim-Centric Reasoning in Logic-Based Argumentation","doi":"10.24963/ijcai.2024/377","publisher":"International Joint Conferences on Artificial Intelligence Organization","date_updated":"2025-09-11T10:02:03Z","author":[{"last_name":"Hecher","full_name":"Hecher, Markus","first_name":"Markus"},{"full_name":"Mahmood, Yasir","id":"99353","last_name":"Mahmood","first_name":"Yasir"},{"first_name":"Arne","last_name":"Meier","full_name":"Meier, Arne"},{"first_name":"Johannes","full_name":"Schmidt, Johannes","last_name":"Schmidt"}],"date_created":"2024-08-19T16:21:51Z","abstract":[{"text":"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.","lang":"eng"}],"status":"public","type":"conference","publication":"Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence","language":[{"iso":"eng"}],"project":[{"_id":"121","name":"TRR 318; TP B01: Ein dialogbasierter Ansatz zur Erklärung von Modellen des maschinellen Lernens"}],"_id":"55655","user_id":"99353","department":[{"_id":"574"}]}