{"type":"ScholarlyArticle","title":"Linear Temporal Logic Modulo Theories over Finite Traces","authors":[{"name":"Luca Geatti","slug":"luca-geatti","given":"Luca","family":"Geatti","affiliation":"Free University of Bozen-Bolzano , Italy"},{"name":"Alessandro Gianola","slug":"alessandro-gianola","given":"Alessandro","family":"Gianola","affiliation":"Free University of Bozen-Bolzano , Italy"},{"name":"Nicola Gigante","slug":"nicola-gigante","given":"Nicola","family":"Gigante","affiliation":"Free University of Bozen-Bolzano , Italy"}],"abstract":"This paper studies Linear Temporal Logic over Finite Traces (LTLf) where proposition letters are replaced with first-order formulas interpreted over arbitrary theories, in the spirit of Satisfiability Modulo Theories. The resulting logic, called LTLf Modulo Theories (LTLfMT), is semi-decidable. Nevertheless, its high expressiveness comes useful in a number of use cases, such as model-checking of data-aware processes and data-aware planning. Despite the general undecidability of these problems, being able to solve satisfiable instances is a compromise worth studying. After motivating and describing such use cases, we provide a sound and complete semi-decision procedure for LTLfMT based on the SMT encoding of a one-pass tree-shaped tableau system. The algorithm is implemented in the BLACK satisfiability checking tool, and an experimental evaluation shows the feasibility of the approach on novel benchmarks.","doi":"10.24963/ijcai.2022/366","url":"https://ijcai.org/proceedings/2022/366","pdf":"https://ijcai.org/proceedings/2022/0366.pdf","markdown":"https://ijcai.org/proceedings/2022/366.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/366","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":366,"pages":{"first":2641,"last":2647},"keywords":["Knowledge Representation and Reasoning: Qualitative, Geometric, Spatial, Temporal Reasoning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986631","length_sec":83,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984774","length_sec":354,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Artale et al., 2019] Alessandro Artale, Andrea Mazzullo, and Ana Ozaki. 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