{"type":"ScholarlyArticle","title":"Anytime Multi-Agent Path Finding via Large Neighborhood Search","authors":[{"name":"Jiaoyang Li","slug":"jiaoyang-li","given":"Jiaoyang","family":"Li","affiliation":""},{"name":"Zhe Chen","slug":"zhe-chen","given":"Zhe","family":"Chen","affiliation":""},{"name":"Daniel Harabor","slug":"daniel-harabor","given":"Daniel","family":"Harabor","affiliation":""},{"name":"Peter J. Stuckey","slug":"peter-j-stuckey","given":"Peter J.","family":"Stuckey","affiliation":""},{"name":"Sven Koenig","slug":"sven-koenig","given":"Sven","family":"Koenig","affiliation":""}],"abstract":"Multi-Agent Path Finding (MAPF) is the challenging problem of computing collision-free paths for multiple agents. Algorithms for solving MAPF can be categorized on a spectrum. At one end are (bounded-sub)optimal algorithms that can find high-quality solutions for small problems. At the other end are unbounded-suboptimal algorithms that can solve large problems but usually find low-quality solutions. In this paper, we consider a third approach that combines the best of both worlds: anytime algorithms that quickly find an initial solution using efficient MAPF algorithms from the literature, even for large problems, and that subsequently improve the solution quality to near-optimal as time progresses by replanning subgroups of agents using Large Neighborhood Search. We compare our algorithm MAPF-LNS against a range of existing work and report significant gains in scalability, runtime to the initial solution, and speed of improving the solution.","doi":"10.24963/ijcai.2021/568","url":"https://ijcai.org/proceedings/2021/568","pdf":"https://ijcai.org/proceedings/2021/0568.pdf","markdown":"https://ijcai.org/proceedings/2021/568.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/568","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Planning and Scheduling","track":"Main Track","number":568,"pages":{"first":4127,"last":4135},"keywords":["Agent-based and Multi-agent Systems: Multi-agent Planning","Planning and Scheduling: Search in Planning and Scheduling","Robotics: Motion and Path Planning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}