{"type":"ScholarlyArticle","title":"Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic (Extended Abstract)","authors":[{"name":"Luigi Bonassi","slug":"luigi-bonassi","given":"Luigi","family":"Bonassi","affiliation":"University of Brescia , Italy"},{"name":"Giuseppe De Giacomo","slug":"giuseppe-de-giacomo","given":"Giuseppe","family":"De Giacomo","affiliation":"University of Oxford , UK; Sapienza University , Italy"},{"name":"Marco Favorito","slug":"marco-favorito","given":"Marco","family":"Favorito","affiliation":"Bank of Italy"},{"name":"Francesco Fuggitti","slug":"francesco-fuggitti","given":"Francesco","family":"Fuggitti","affiliation":"Bank of Italy"},{"name":"Alfonso Emilio Gerevini","slug":"alfonso-emilio-gerevini","given":"Alfonso Emilio","family":"Gerevini","affiliation":"University of Brescia , Italy"},{"name":"Enrico Scala","slug":"enrico-scala","given":"Enrico","family":"Scala","affiliation":"University of Brescia , Italy"}],"abstract":"We study classical planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (PPLTL). \r\nPPLTL is as expressive as Linear-time Temporal Logic on finite traces (LTLf), but as shown in this paper, it is computationally much better behaved for planning. \r\nSpecifically, we show that planning for PPLTL goals can be encoded into classical planning with minimal overhead, introducing only a number of new fluents that is at most linear in the PPLTL goal and no spurious additional actions. \r\nBased on these results, we implemented a system called Plan4Past, which can be used along with state-of-the-art classical planners, such as LAMA.\r\nAn empirical analysis demonstrates the practical effectiveness of Plan4Past, showing that a classical planner generally performs better with our compilation than with other existing compilations for LTLf goals over the considered benchmarks.","doi":"10.24963/ijcai.2024/926","url":"https://ijcai.org/proceedings/2024/926","pdf":"https://ijcai.org/proceedings/2024/0926.pdf","markdown":"https://ijcai.org/proceedings/2024/926.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/926","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":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":926,"pages":{"first":8378,"last":8383},"keywords":["Planning and Scheduling: General","Knowledge Representation and Reasoning: KRR: Knowledge representation languages","Knowledge Representation and Reasoning: KRR: Qualitative, geometric, spatial, and temporal reasoning"],"videos":[],"references":[{"position":1,"raw":"[Alechina et al., 2018] Natasha Alechina, Brian Logan, and Mehdi Dastani. 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