{"type":"ScholarlyArticle","title":"Angluin-Style Learning of Deterministic Büchi and Co-Büchi Automata","authors":[{"name":"Yong Li","slug":"yong-li","given":"Yong","family":"Li","affiliation":"Department of Computer Science , University of Liverpool; Key Laboratory of System Software (Chinese Academy of Sciences) and"},{"name":"Sven Schewe","slug":"sven-schewe","given":"Sven","family":"Schewe","affiliation":"Department of Computer Science , University of Liverpool"},{"name":"Qiyi Tang","slug":"qiyi-tang","given":"Qiyi","family":"Tang","affiliation":"Department of Computer Science , University of Liverpool; State Key Laboratory of Computer Science , Institute of Software , Chinese Academy of Sciences"}],"abstract":"While recently developed Angluin-style learning algorithms for omega-automata have much in common with her classic DFA learning algorithm, there is a huge difference in the cost of the equivalence queries about the target automata.\r\nFor omega-regular languages, the target is to learn nondeterministic Buchi automata (NBAs) through the vehicle of Families of DFAs (FDFAs).\r\nWhile the cost of equivalence queries is usually idealised as constant in learning, it makes a practical difference that the language equivalence checking about the learned NBAs is computationally hard.\r\n\r\nWe develop efficient techniques for the cases, where we learn deterministic Buchi automata (DBAs) or deterministic co-Buchi automata (DCAs).\r\nThis is based on the observation that some classes of FDFAs can be used to learn DBAs for DBA recognisable languages, rather than having to resort to nondeterministic ones.\r\nWe believe that the restriction to DBAs and DCAs in equivalence queries also makes our algorithm more appealing to realistic applications, as the operations are cheap---NL---for DBAs and DCAs.","doi":"10.24963/ijcai.2024/498","url":"https://ijcai.org/proceedings/2024/498","pdf":"https://ijcai.org/proceedings/2024/0498.pdf","markdown":"https://ijcai.org/proceedings/2024/498.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/498","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":"Machine Learning","track":"Main Track","number":498,"pages":{"first":4506,"last":4514},"keywords":["Machine Learning: ML: Active learning","Machine Learning: ML: Model-based and model learning reinforcement learning","Knowledge Representation and Reasoning: KRR: Automated reasoning and theorem proving","Knowledge Representation and Reasoning: KRR: Learning and reasoning"],"videos":[],"references":[{"position":1,"raw":"[Angluin and Fisman, 2016] Dana Angluin and Dana Fis- man. 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