Proceedings

A Lightweight Traffic Map for Efficient Anytime LaCAM

Bojie Shen, Yue Zhang, Zhe Chen, Daniel Harabor

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 332-339.

https://doi.org/10.24963/ijcai.2026/38

Keywords

  • Agent-based and Multi-agent Systems: Multi-agent planning
  • Search: Heuristic search

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.

Bojie Shen, Yue Zhang, Zhe Chen, and Daniel Harabor. 2026. A Lightweight Traffic Map for Efficient Anytime LaCAM. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 332–339. https://doi.org/10.24963/ijcai.2026/38
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