{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T07:01:23Z","timestamp":1762326083380,"version":"build-2065373602"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>Assumption-Based Argumentation (ABA) is a prominent formalism for structured argumentation, widely applied in domains such as healthcare, law, and robotics.\n\nDespite its inherent computational complexity, ABA has seen the development of effective techniques that successfully address key tasks, including evaluating the acceptability of literals and computing framework extensions.\n\nThese approaches typically involve translating the initial ABA framework into an intermediate formalism, such as an Answer Set Program or an Abstract Argumentation Framework, which is then encoded into a Boolean satisfiability (SAT) problem.\n\nHowever, this translation can lead to large and complex intermediate representations, posing challenges for state-of-the-art SAT solvers.\n\nIn this work, we propose a Counterexample-Guided Abstraction Refinement (CEGAR) approach that bypasses the initial translation step, at the cost of incrementally discovering certain ABA constraints that are not explicitly captured in the initial SAT encoding.\n\nWe analyze the performance of our method and demonstrate that it outperforms state-of-the-art approaches on specific problem classes, while remaining competitive with the best existing solvers more broadly.<\/jats:p>","DOI":"10.24963\/kr.2025\/67","type":"proceedings-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:10:44Z","timestamp":1762323044000},"page":"694-706","source":"Crossref","is-referenced-by-count":0,"title":["Counterexample-Guided Abstraction Refinement for Assumption-based Argumentation"],"prefix":"10.24963","author":[{"given":"Jean Marie","family":"Lagniez","sequence":"first","affiliation":[{"name":"CRIL, Universit\u00e9 d'Artois & CNRS"}]},{"given":"Emmanuel","family":"Lonca","sequence":"additional","affiliation":[{"name":"CRIL, Universit\u00e9 d'Artois & CNRS"}]},{"given":"Jean-Guy","family":"Mailly","sequence":"additional","affiliation":[{"name":"IRIT, Universit\u00e9 Toulouse Capitole & CNRS"}]}],"member":"10584","event":{"name":"22nd International Conference on Principles of Knowledge Representation and Reasoning {KR-2025}","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"KR-2025","number":"22","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"start":{"date-parts":[[2025,11,11]]},"end":{"date-parts":[[2025,11,17]]}},"container-title":["Proceedings of the TwentySecond International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:11:20Z","timestamp":1762323080000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2025\/67"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2025\/67","relation":{},"subject":[],"published":{"date-parts":[[2025,11]]}}}