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arXiv AI By Viraj Parimi, Shao-Hung Chan, Han Zhang, Jingkai Chen, Brian Williams

Belief-Aware Multi-Agent Path Finding under Map Uncertainty

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The paper introduces Belief-Aware Multi-Agent Path Finding under Map Uncertainty, addressing the challenge that real-world environments can change unexpectedly. It proposes MAGIC, a framework that uses a Gaussian Markov Random Field and Gaussian Belief Propagation to update a shared belief about traversability online, allowing agents to infer the state of nearby unobserved locations. Experiments on standard MAPF benchmarks show that MAGIC reduces the executed sum of costs on 96.3% of instances, outperforming existing approaches across various planner families and large agent teams.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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