{"type":"ScholarlyArticle","title":"Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators","authors":[{"name":"Guillem Francès","slug":"guillem-frances","given":"Guillem","family":"Francès","affiliation":"Universitat Pompeu Fabra , Spain;"},{"name":"Miquel Ramírez","slug":"miquel-ramirez","given":"Miquel","family":"Ramírez","affiliation":"University of Melbourne , Australia;"},{"name":"Nir Lipovetzky","slug":"nir-lipovetzky","given":"Nir","family":"Lipovetzky","affiliation":"University of Melbourne , Australia;"},{"name":"Hector Geffner","slug":"hector-geffner","given":"Hector","family":"Geffner","affiliation":"Universitat Pompeu Fabra , Spain;; ICREA"}],"abstract":"Classical planning is concerned with problems where a goal needs to be reached\r\nfrom a known initial state by doing actions with deterministic, known effects.\r\nClassical planners, however, deal only with classical problems that can be\r\nexpressed in declarative planning languages such as STRIPS or PDDL. This\r\nprevents their use on problems that are not easy to model declaratively or\r\nwhose dynamics are given via simulations. Simulators do not provide a\r\ndeclarative representation of actions, but simply return successor states.\r\nThe question we address in this paper is: can a planner that has access to\r\nthe structure of states and goals only, approach the performance of planners\r\nthat also have access to the structure of actions expressed in PDDL?\r\nTo answer this, we develop domain-independent, black box planning algorithms\r\nthat completely ignore action structure, and show that they match the\r\nperformance of state-of-the-art classical planners on the standard planning\r\nbenchmarks. Effective black box algorithms open up new possibilities for\r\nmodeling and for expressing control knowledge, which we also illustrate.","doi":"10.24963/ijcai.2017/600","url":"https://ijcai.org/proceedings/2017/600","pdf":"https://ijcai.org/proceedings/2017/0600.pdf","markdown":"https://ijcai.org/proceedings/2017/600.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/600","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Planning and Scheduling","track":"Main track","number":600,"pages":{"first":4294,"last":4301},"keywords":["Planning and Scheduling: Planning Algorithms","Planning and Scheduling: Search in Planning and Scheduling"],"videos":[],"references":[{"position":1,"raw":"[Bellemare et al., 2013] Marc Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. 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