{"type":"ScholarlyArticle","title":"Anytime Focal Search with Applications","authors":[{"name":"Liron Cohen","slug":"liron-cohen","given":"Liron","family":"Cohen","affiliation":"University of Southern California"},{"name":"Matias Greco","slug":"matias-greco","given":"Matias","family":"Greco","affiliation":"Universidad Andres Bello"},{"name":"Hang Ma","slug":"hang-ma","given":"Hang","family":"Ma","affiliation":"University of Southern California"},{"name":"Carlos Hernandez","slug":"carlos-hernandez","given":"Carlos","family":"Hernandez","affiliation":"Universidad Andres Bello"},{"name":"Ariel Felner","slug":"ariel-felner","given":"Ariel","family":"Felner","affiliation":"Ben-Gurion University"},{"name":"T. K. Satish Kumar","slug":"t-k-satish-kumar","given":"T. K. Satish","family":"Kumar","affiliation":"University of Southern California"},{"name":"Sven Koenig","slug":"sven-koenig","given":"Sven","family":"Koenig","affiliation":"University of Southern California"}],"abstract":"Focal search (FS) is a bounded-suboptimal search (BSS) variant of A*. Like A*, it uses an open list whose states are sorted in increasing order of their f-values. Unlike A*, it also uses a focal list containing all states from the open list whose f-values are no larger than a suboptimality factor times the smallest f-value in the open list. In this paper, we develop an anytime version of FS, called anytime FS (AFS), that is useful when deliberation time is limited. AFS finds a \"good\" solution quickly and refines it to better and better solutions if time allows. It does this refinement efficiently by reusing previous search efforts. On the theoretical side, we show that AFS is bounded suboptimal and that anytime potential search (ATPS/ANA*), a state-of-the-art anytime bounded-cost search (BCS) variant of A*, is a special case of AFS. In doing so, we bridge the gap between anytime search algorithms based on BSS and BCS. We also identify different properties of priority functions, used to sort the focal list, that may allow for efficient reuse of previous search efforts. On the experimental side, we demonstrate the usefulness of AFS for solving hard combinatorial problems, such as the generalized covering traveling salesman problem and the multi-agent pathfinding problem.","doi":"10.24963/ijcai.2018/199","url":"https://ijcai.org/proceedings/2018/199","pdf":"https://ijcai.org/proceedings/2018/0199.pdf","markdown":"https://ijcai.org/proceedings/2018/199.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/199","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Heuristic Search and Game Playing","track":"Main track","number":199,"pages":{"first":1434,"last":1441},"keywords":["Heuristic Search and Game Playing: Combinatorial Search and Optimisation","Heuristic Search and Game Playing: Heuristic Search"],"videos":[],"references":[{"position":1,"raw":"[Aine and Likhachev, 2016] Sandip Aine and Maxim Likhachev. 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