{"type":"ScholarlyArticle","title":"Multi-objective Search via Lazy and Efficient Dominance Checks","authors":[{"name":"Carlos Hernández","slug":"carlos-hernandez","given":"Carlos","family":"Hernandez","affiliation":"Universidad San Sebastián , Santiago , Chile"},{"name":"William Yeoh","slug":"william-yeoh","given":"William","family":"Yeoh","affiliation":"Washington University in St. Louis , Saint Louis , USA"},{"name":"Jorge A. Baier","slug":"jorge-a-baier","given":"Jorge A.","family":"Baier","affiliation":"Pontificia Universidad Católica de Chile , Santiago , Chile"},{"name":"Ariel Felner","slug":"ariel-felner","given":"Ariel","family":"Felner","affiliation":"Ben-Gurion University of the Negev , Beersheba , Israel"},{"name":"Oren Salzman","slug":"oren-salzman","given":"Oren","family":"Salzman","affiliation":"Technion-Israel Institute of Technology , Haifa , Israel"},{"name":"Han Zhang","slug":"han-zhang","given":"Han","family":"Zhang","affiliation":"University of Southern California , Los Angeles , USA"},{"name":"Shao-Hung Chan","slug":"shao-hung-chan","given":"Shao-Hung","family":"Chan","affiliation":"University of Southern California , Los Angeles , USA"},{"name":"Sven Koenig","slug":"sven-koenig","given":"Sven","family":"Koenig","affiliation":"University of Southern California , Los Angeles , USA"}],"abstract":"Multi-objective search can be used to model many real-world problems that require finding Pareto optimal paths from a specified start state to a specified goal state, while considering different costmetrics such as distance, time, and fuel. The performance of multi-objective search can be improved by making dominance checking—an operation necessary to determine whether or not a path dominates another—more efficient. This was shown in practice by BOA*, a state-of-the-art bi-objective search algorithm, which outperforms previously existing bi-objective search algorithms in part because it adopts a lazy approach towards dominance checking. EMOA*, a recent multi-objective search algorithm, generalizes BOA* to more-than-two objectives using AVL trees for dominance checking.\r\nIn this paper, we first propose Linear-Time Multi-Objective A* (LTMOA*), an multi-objective search algorithm that implements a more efficient dominance checking than EMOA* using simple data structures like arrays. We then propose an even lazier approach towards dominance checking, and the resulting algorithm, LazyLTMOA*, distinguishes from EMOA* and LTMOA* by removing the dominance checking during node generation. Our experimental results show that LazyLTMOA* outperforms EMOA* by up to an order of magnitude in terms of runtime.","doi":"10.24963/ijcai.2023/850","url":"https://ijcai.org/proceedings/2023/850","pdf":"https://ijcai.org/proceedings/2023/0850.pdf","markdown":"https://ijcai.org/proceedings/2023/850.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/850","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Late Papers","track":"Main Track","number":850,"pages":{"first":7223,"last":7230},"keywords":["Search: S: Heuristic search","Robotics: ROB: Motion and path planning","Search: S: Combinatorial search and optimisation"],"videos":[],"references":[{"position":1,"raw":"[Ahmadi et al., 2021] Saman Ahmadi, Guido Tack, Daniel Harabor, and Philip Kilby. 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