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Learn all about Multi-Agent Path Finding (MAPF)

Publication

H. Zhang, M. Yao, Z. Liu, J. Li, L. Terr, S.-H. Chan, S. Kumar and S. Koenig. A Hierarchical Approach to Multi-Agent Path Finding [Abstract]. In Proceedings of the Symposium on Combinatorial Search (SoCS), 2021.


Abstract: Solving Multi-Agent Path Finding (MAPF) instances optimally is NP-hard, and existing optimal and bounded suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multip le regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP is able to solve as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.


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(last updated in 2020)