{"type":"ScholarlyArticle","title":"MGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding Problem","authors":[{"name":"Mingkai Tang","slug":"mingkai-tang","given":"Mingkai","family":"Tang","affiliation":"Hong Kong University of Science and Technology"},{"name":"Yuanhang Li","slug":"yuanhang-li","given":"Yuanhang","family":"Li","affiliation":"Hong Kong University of Science and Technology"},{"name":"Hongji Liu","slug":"hongji-liu","given":"Hongji","family":"Liu","affiliation":"Hong Kong University of Science and Technology"},{"name":"Yingbing Chen","slug":"yingbing-chen","given":"Yingbing","family":"Chen","affiliation":"Hong Kong University of Science and Technology"},{"name":"Ming Liu","slug":"ming-liu","given":"Ming","family":"Liu","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Lujia Wang","slug":"lujia-wang","given":"Lujia","family":"Wang","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"}],"abstract":"With the expansion of the scale of robotics applications, the multi-goal multi-agent pathfinding (MG-MAPF) problem began to gain widespread attention. This problem requires each agent to visit pre-assigned multiple goal points at least once without conflict. Some previous methods have been proposed to solve the MG-MAPF problem based on Decoupling the goal Vertex visiting order search and the Single-agent pathfinding (DVS). However, this paper demonstrates that the methods based on DVS cannot always obtain the optimal solution. To obtain the optimal result, we propose the Multi-Goal Conflict-Based Search (MGCBS), which is based on Decoupling the goal Safe interval visiting order search and the Single-agent pathfinding (DSS). Additionally, we present the Time-Interval-Space Forest (TIS Forest) to enhance the efficiency of MGCBS by maintaining the shortest paths from any start point at any start time step to each safe interval at the goal points. The experiment demonstrates that our method can consistently obtain optimal results and execute up to 7 times faster than the state-of-the-art method in our evaluation.","doi":"10.24963/ijcai.2024/28","url":"https://ijcai.org/proceedings/2024/28","pdf":"https://ijcai.org/proceedings/2024/0028.pdf","markdown":"https://ijcai.org/proceedings/2024/28.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/28","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":28,"pages":{"first":249,"last":256},"keywords":["Agent-based and Multi-agent Systems: MAS: Multi-agent planning","Search: S: Combinatorial search and optimisation","Robotics: ROB: Multi-robot systems","Planning and Scheduling: PS: Robot planning"],"videos":[],"references":[{"position":1,"raw":"[Applegate et al., 2011] David L Applegate, Robert E Bixby, Vašek Chvátal, and William J Cook. 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