{"type":"ScholarlyArticle","title":"A Lightweight Traffic Map for Efficient Anytime LaCAM","authors":[{"name":"Bojie Shen","slug":"bojie-shen","given":"Bojie","family":"Shen","affiliation":""},{"name":"Yue Zhang","slug":"yue-zhang","given":"Yue","family":"Zhang","affiliation":""},{"name":"Zhe Chen","slug":"zhe-chen","given":"Zhe","family":"Chen","affiliation":""},{"name":"Daniel Harabor","slug":"daniel-harabor","given":"Daniel","family":"Harabor","affiliation":""}],"abstract":"Multi-Agent Path Finding (MAPF) seeks collision-free paths for teams of agents and has a wide range of practical applications. LaCAM*, an anytime configuration-based solver, currently represents the state-of-the-art. Recent work has explored using guidance paths to steer LaCAM* toward configurations that avoid traffic congestion, thereby improving solution quality. However, existing approaches rely on Frank–Wolfe–style optimisation to repeatedly invoke single-agent search before executing LaCAM*, which creates a large computational overhead in large-scale problems. The guide path is also static, which is only helpful for finding the first solution in LaCAM*. To overcome this problem, we propose a new approach that exploits LaCAM*’s ability to construct a dynamic, lightweight traffic map during LaCAM*'s search. Experiments show that our method achieves higher solution quality than state-of-the-art guidance-path approaches in two variants of MAPF problems.","doi":"10.24963/ijcai.2026/38","url":"https://ijcai.org/proceedings/2026/38","pdf":"https://ijcai.org/proceedings/2026/0038.pdf","markdown":"https://ijcai.org/proceedings/2026/38.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/38","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":38,"pages":{"first":332,"last":339},"keywords":["Agent-based and Multi-agent Systems: Multi-agent planning","Search: Heuristic search"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}