{"type":"ScholarlyArticle","title":"Optimal LTLf Synthesis","authors":[{"name":"Yujian Cao","slug":"yujian-cao","given":"Yujian","family":"Cao","affiliation":"University of Liverpool"},{"name":"Sven Schewe","slug":"sven-schewe","given":"Sven","family":"Schewe","affiliation":"University of Liverpool"},{"name":"Qiyi Tang","slug":"qiyi-tang","given":"Qiyi","family":"Tang","affiliation":"University of Liverpool"},{"name":"Shufang Zhu","slug":"shufang-zhu","given":"Shufang","family":"Zhu","affiliation":"University of Liverpool"}],"abstract":"Strategy synthesis typically follows an all-or-nothing paradigm, returning unrealisable whenever a specification cannot be guaranteed in an uncertain environment. In this paper, we introduce optimal LTLf synthesis, where the goal is to realise as many objectives as possible from a given specification consisting of multiple objectives, especially for the case that they are not all jointly realisable. We first consider max-guarantee synthesis, which commits to a maximal set of objectives that we can a priori guarantee to realise. We then introduce max-observation synthesis, which maximises a posteriori realised objectives that may be incomparable on different executions. Finally, we present incremental max-observation synthesis, which further improves strategies by exploiting opportunities for stronger guarantees when they arise during an execution. Experimental results show that different variations of optimal synthesis scale broadly equally well, solving a large fraction of the benchmark instances within the given timeout, demonstrating the practical feasibility of the approach.","doi":"10.24963/ijcai.2026/4","url":"https://ijcai.org/proceedings/2026/4","pdf":"https://ijcai.org/proceedings/2026/0004.pdf","markdown":"https://ijcai.org/proceedings/2026/4.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/4","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":4,"pages":{"first":28,"last":35},"keywords":["Agent-based and Multi-agent Systems: Formal verification, validation and synthesis","Planning and Scheduling: Real-time planning","Planning and Scheduling: Theoretical foundations of planning","Planning and Scheduling: Planning algorithms"],"videos":[],"references":[{"position":1,"raw":"[Aminof et al., 2019] Benjamin Aminof, Giuseppe De Gia- como, Aniello Murano, and Sasha Rubin. 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