{"type":"ScholarlyArticle","title":"Planning with Qualitative Action-Trajectory Constraints in PDDL","authors":[{"name":"Luigi Bonassi","slug":"luigi-bonassi","given":"Luigi","family":"Bonassi","affiliation":"Dipartimento di Ingegneria dell'Informazione , Università degli Studi di Brescia , Italy"},{"name":"Alfonso Emilio Gerevini","slug":"alfonso-emilio-gerevini","given":"Alfonso Emilio","family":"Gerevini","affiliation":"Dipartimento di Ingegneria dell'Informazione , Università degli Studi di Brescia , Italy"},{"name":"Enrico Scala","slug":"enrico-scala","given":"Enrico","family":"Scala","affiliation":"Dipartimento di Ingegneria dell'Informazione , Università degli Studi di Brescia , Italy"}],"abstract":"In automated planning the ability of expressing constraints on the structure of the desired plans is important to deal with solution quality, as well as to express control knowledge. In PDDL3, this is supported through state-trajectory constraints corresponding to a class of LTLf formulae. In this paper,  first we introduce a formalism to express trajectory constraints over actions in the plan, rather than over traversed states; Then we investigate compilation-based methods to deal with such constraints in propositional planning, and propose a new simple effective method. Finally, we experimentally study the usefulness of our action-trajectory constraints as a tool to express control knowledge. The experimental results show that the performance of a classical planner can be significantly improved by exploiting knowledge expressed by action constraints and handled by our compilation method, while the same knowledge turns out to be less beneficial when specified as state constraints and handled by two state-of-the-art systems supporting state constraints.","doi":"10.24963/ijcai.2022/639","url":"https://ijcai.org/proceedings/2022/639","pdf":"https://ijcai.org/proceedings/2022/0639.pdf","markdown":"https://ijcai.org/proceedings/2022/639.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/639","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":639,"pages":{"first":4606,"last":4613},"keywords":["Planning and Scheduling: Planning Algorithms","Planning and Scheduling: Search in Planning and Scheduling","Knowledge Representation and Reasoning: Knowledge Representation Languages"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986910","length_sec":82,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985053","length_sec":329,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Baier and McIlraith, 2006] Jorge A. 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