---
title: "A Lightweight Traffic Map for Efficient Anytime LaCAM"
authors:
  - "Bojie Shen"
  - "Yue Zhang"
  - "Zhe Chen"
  - "Daniel Harabor"
doi: "10.24963/ijcai.2026/38"
url: "https://ijcai.org/proceedings/2026/38"
pdf: "https://ijcai.org/proceedings/2026/0038.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "332-339"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: Multi-agent planning"
  - "Search: Heuristic search"
---

# A Lightweight Traffic Map for Efficient Anytime LaCAM

Bojie Shen, Yue Zhang, Zhe Chen, Daniel Harabor

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 332-339. DOI: [10.24963/ijcai.2026/38](https://doi.org/10.24963/ijcai.2026/38)

## 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.