{"type":"ScholarlyArticle","title":"Symbolic LTLf Synthesis","authors":[{"name":"Shufang Zhu","slug":"shufang-zhu","given":"Shufang","family":"Zhu","affiliation":"East China Normal University"},{"name":"Lucas M. Tabajara","slug":"lucas-m-tabajara","given":"Lucas M.","family":"Tabajara","affiliation":"Rice University"},{"name":"Jianwen Li","slug":"jianwen-li","given":"Jianwen","family":"Li","affiliation":"Rice University"},{"name":"Geguang Pu","slug":"geguang-pu","given":"Geguang","family":"Pu","affiliation":"† East China Normal University"},{"name":"Moshe Y. Vardi","slug":"moshe-y-vardi","given":"Moshe Y.","family":"Vardi","affiliation":"Rice University"}],"abstract":"LTLf synthesis is the process of finding a strategy that satisfies a linear temporal specification over finite traces. An existing solution to this problem relies on a reduction to a DFA game. In this paper, we propose a symbolic framework for LTLf synthesis based on this technique, by performing the computation over a representation of the DFA as a boolean formula rather than as an explicit graph. This approach enables strategy generation by utilizing the mechanism of boolean synthesis. We implement this symbolic synthesis method in a tool called Syft, and demonstrate by experiments on scalable benchmarks that the symbolic approach scales better than the explicit one.","doi":"10.24963/ijcai.2017/189","url":"https://ijcai.org/proceedings/2017/189","pdf":"https://ijcai.org/proceedings/2017/0189.pdf","markdown":"https://ijcai.org/proceedings/2017/189.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/189","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":189,"pages":{"first":1362,"last":1369},"keywords":["Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation","Knowledge Representation, Reasoning, and Logic: Game Theory","Knowledge Representation, Reasoning, and Logic: Automated Reasoning and Theorem Proving"],"videos":[],"references":[{"position":1,"raw":"[Bacchus and Kabanza, 2000] Fahiem Bacchus and Frodu- ald Kabanza. 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