{"type":"ScholarlyArticle","title":"Path Planning with CPD Heuristics","authors":[{"name":"Massimo Bono","slug":"massimo-bono","given":"Massimo","family":"Bono","affiliation":"Dipartimento di Ingegneria dell'Informazione , Università degli Studi di Brescia , Brescia , Italy"},{"name":"Alfonso E. Gerevini","slug":"alfonso-e-gerevini","given":"Alfonso E.","family":"Gerevini","affiliation":"Dipartimento di Ingegneria dell'Informazione , Università degli Studi di Brescia , Brescia , Italy"},{"name":"Daniel D. Harabor","slug":"daniel-d-harabor","given":"Daniel D.","family":"Harabor","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"},{"name":"Peter J. Stuckey","slug":"peter-j-stuckey","given":"Peter J.","family":"Stuckey","affiliation":"Faculty of Information Technology , Monash University , Melbourne , Australia"}],"abstract":"Compressed Path Databases (CPDs) are a leading technique for optimal pathfinding in graphs with static edge costs.  In this work we investigate CPDs as admissible heuristic functions and we apply them in two distinct settings: problems where the graph is subject to dynamically changing costs, and anytime settings where deliberation time is limited.  Conventional heuristics derive cost-to-go estimates by reasoning about a tentative and usually infeasible path, from the current node to the target.  CPD-based heuristics derive cost-to-go estimates by computing a concrete and usually feasible path.  We exploit such paths to bound the optimal solution, not just from below but also from above.  We demonstrate the benefit of this approach in a range of experiments on standard gridmaps and in comparison to Landmarks, a popular alternative also developed for searching in explicit state-spaces.","doi":"10.24963/ijcai.2019/167","url":"https://ijcai.org/proceedings/2019/167","pdf":"https://ijcai.org/proceedings/2019/0167.pdf","markdown":"https://ijcai.org/proceedings/2019/167.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/167","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Heuristic Search and Game Playing","track":"Main track","number":167,"pages":{"first":1199,"last":1205},"keywords":["Heuristic Search and Game Playing: Heuristic Search","Robotics: Motion and Path Planning"],"videos":[],"references":[{"position":1,"raw":"[Abraham et al., 2012] Ittai Abraham, Daniel Delling, An- drew V. 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