Publication

J. Li, Z. Chen, D. Harabor, P. Stuckey and S. Koenig. MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2022.

Abstract: Multi-Agent Path Finding (MAPF) is the problem of planning collision-free paths for multiple agents in a shared environment. In this paper, we propose a novel algorithm LNS2 based on large neighborhood search for solving MAPF efficiently. Starting from a set of paths that contain collisions, LNS2 repeatedly selects a subset of colliding agents and replans their paths to reduce the number of collisions until the paths become collision-free. We compare LNS2 against a variety of state-of-the-art MAPF algorithms, including Prioritized Planning with random restarts, EECBS, and PPS, and show that LNS2 runs significantly faster than them while still providing near-optimal solutions in most cases. With a runtime limit of just 5 minutes, LNS2 solves 80 percent of the random-scenario instances with the largest number of agents from the MAPF benchmark suite with a runtime limit of just 5 minutes, which, to our knowledge, has not been achieved by any existing algorithms.

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