{"type":"ScholarlyArticle","title":"Learning and Exploiting Progress States in Greedy Best-First Search","authors":[{"name":"Patrick Ferber","slug":"patrick-ferber","given":"Patrick","family":"Ferber","affiliation":"University of Basel , Basel , Switzerland; Saarland University , Saarland Informatics Campus , Saarbrücken , Germany"},{"name":"Liat Cohen","slug":"liat-cohen","given":"Liat","family":"Cohen","affiliation":"University of Basel , Basel , Switzerland"},{"name":"Jendrik Seipp","slug":"jendrik-seipp","given":"Jendrik","family":"Seipp","affiliation":"Linköping University , Linköping , Sweden"},{"name":"Thomas Keller","slug":"thomas-keller","given":"Thomas","family":"Keller","affiliation":"University of Basel , Basel , Switzerland; University of Zurich , Zurich , Switzerland"}],"abstract":"Previous work introduced the concept of progress\r\nstates. After expanding a progress state, a greedy\r\nbest-first search (GBFS) will only expand states\r\nwith lower heuristic values. Current methods can\r\nidentify progress states only for a single task and\r\nonly after a solution for the task has been found.\r\nWe introduce a novel approach that learns a description\r\nlogic formula characterizing all progress\r\nstates in a classical planning domain. Using the\r\nlearned formulas in a GBFS to break ties in favor\r\nof progress states often significantly reduces the\r\nsearch effort.","doi":"10.24963/ijcai.2022/657","url":"https://ijcai.org/proceedings/2022/657","pdf":"https://ijcai.org/proceedings/2022/0657.pdf","markdown":"https://ijcai.org/proceedings/2022/657.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/657","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":"Search","track":"Main Track","number":657,"pages":{"first":4740,"last":4746},"keywords":["Planning and Scheduling: Learning in Planning and Scheduling","Planning and Scheduling: Search in Planning and Scheduling","Search: Search and Machine Learning","Search: Heuristic Search"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986722","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984865","length_sec":369,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Baader et al., 2003] Franz Baader, Diego Calvanese, Deb- orah L. 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