{"type":"ScholarlyArticle","title":"Explaining the Behaviour of Hybrid Systems with PDDL+ Planning","authors":[{"name":"Diego Aineto","slug":"diego-aineto","given":"Diego","family":"Aineto","affiliation":"Universitat Politècnica de València"},{"name":"Eva Onaindia","slug":"eva-onaindia","given":"Eva","family":"Onaindia","affiliation":"Universitat Politècnica de València"},{"name":"Miquel Ramirez","slug":"miquel-ramirez","given":"Miquel","family":"Ramírez","affiliation":"University of Melbourne"},{"name":"Enrico Scala","slug":"enrico-scala","given":"Enrico","family":"Scala","affiliation":"Università degli Studi di Brescia"},{"name":"Ivan Serina","slug":"ivan-serina","given":"Ivan","family":"Serina","affiliation":"Università degli Studi di Brescia"}],"abstract":"The aim of this work is to explain the observed behaviour of a hybrid system (HS). The explanation problem is cast as finding a trajectory of the HS that matches some observations. By using the formalism of hybrid automata (HA), we characterize the explanations as the language of a network of HA that comprises one automaton for the HS and another one for the observations, thus restricting the behaviour of the HS exclusively to trajectories that explain the observations. We observe that this problem corresponds to a reachability problem in model-checking, but that state-of-the-art model checkers struggle to find concrete trajectories. To overcome this issue we provide a formal mapping from HA to PDDL+ and show how to use an off-the-shelf automated planner. An experimental analysis over domains with piece-wise constant, linear and nonlinear dynamics reveals that the proposed PDDL+ approach is much more efficient than solving directly the explanation problem with model-checking solvers.","doi":"10.24963/ijcai.2022/634","url":"https://ijcai.org/proceedings/2022/634","pdf":"https://ijcai.org/proceedings/2022/0634.pdf","markdown":"https://ijcai.org/proceedings/2022/634.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/634","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":"Planning and Scheduling","track":"Main Track","number":634,"pages":{"first":4567,"last":4573},"keywords":["Planning and Scheduling: Activity and Plan Recognition","Planning and Scheduling: Mixed Discrete/Continuous Planning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986898","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985041","length_sec":344,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Aineto et al., 2020] Diego Aineto, Sergio Jimenez, and Eva Onaindia. 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