{"type":"ScholarlyArticle","title":"Understanding Subgoal Graphs by Augmenting Contraction Hierarchies","authors":[{"name":"Tansel Uras","slug":"tansel-uras","given":"Tansel","family":"Uras","affiliation":"Department of Computer Science , University of Southern California , Los Angeles , USA"},{"name":"Sven Koenig","slug":"sven-koenig","given":"Sven","family":"Koenig","affiliation":"Department of Computer Science , University of Southern California , Los Angeles , USA"}],"abstract":"Contraction hierarchies and (N-level) subgoal graphs are two preprocessing-based path-planning algorithms that have so far only been compared experimentally through the grid-based path-planning competitions, where both algorithms had undominated runtime/memory trade-offs. Subgoal graphs can be considered as a framework that can be customized to different domains through the choice of a reachability relation R that identifies pairs of nodes on a graph between which it is easy to find shortest paths. Subgoal graphs can exploit R in various ways to speed-up query times and reduce memory requirements. In this paper, we break down the differences between N-level subgoal graphs and contraction hierarchies, and augment contraction hierarchies with ideas from subgoal graphs to exploit R. We propose three different modifications, analyze their runtime/memory trade-offs, and provide experimental results on grids using canonical-freespace-reachability as R, which show that both N-level subgoal graphs and contraction hierarchies are dominated in terms of the runtime/memory trade-off by some of our new variants.","doi":"10.24963/ijcai.2018/209","url":"https://ijcai.org/proceedings/2018/209","pdf":"https://ijcai.org/proceedings/2018/0209.pdf","markdown":"https://ijcai.org/proceedings/2018/209.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/209","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":209,"pages":{"first":1506,"last":1513},"keywords":["Heuristic Search and Game Playing: Heuristic Search","Robotics: Motion and Path Planning"],"videos":[],"references":[{"position":1,"raw":"[Abraham et al., 2010] Ittai Abraham, Amos Fiat, An- drew V. 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