{"type":"ScholarlyArticle","title":"Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning","authors":[{"name":"Pascal Lauer","slug":"pascal-lauer","given":"Pascal","family":"Lauer","affiliation":""},{"name":"Alvaro Torralba","slug":"alvaro-torralba","given":"Álvaro","family":"Torralba","affiliation":""},{"name":"Daniel Fišer","slug":"daniel-fiser","given":"Daniel","family":"Fišer","affiliation":""},{"name":"Daniel Höller","slug":"daniel-holler","given":"Daniel","family":"Höller","affiliation":""},{"name":"Julia Wichlacz","slug":"julia-wichlacz","given":"Julia","family":"Wichlacz","affiliation":""},{"name":"Jörg Hoffmann","slug":"jorg-hoffmann","given":"Jörg","family":"Hoffmann","affiliation":""}],"abstract":"Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases where the grounded representation is \"small enough\". Previous attempts to tackle this problem for the delete relaxation leveraged symmetries to reduce the blow-up. Here we take a more radical approach, applying an additional relaxation to obtain a heuristic function that runs in time polynomial in the size of the PDDL input. Our relaxation splits the predicates into smaller predicates of fixed arity K. We show that computing a relaxed plan is still NP-hard (in PDDL input size) for K>=2, but is polynomial-time for K=1. We implement a heuristic function for K=1 and show that it can improve the state of the art on benchmarks whose grounded representation is large.","doi":"10.24963/ijcai.2021/567","url":"https://ijcai.org/proceedings/2021/567","pdf":"https://ijcai.org/proceedings/2021/0567.pdf","markdown":"https://ijcai.org/proceedings/2021/567.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/567","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Planning and Scheduling","track":"Main Track","number":567,"pages":{"first":4119,"last":4126},"keywords":["Planning and Scheduling: Planning Algorithms","Planning and Scheduling: Search in Planning and Scheduling"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}