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H. Zhang, J. Li, P. Surynek, S. Koenig and S. Kumar. Multi-Agent Path Finding with Mutex Propagation. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages (in print), 2020. Outstanding ICAPS Student Paper Award.

Abstract: Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in Conflict-Based Search (CBS), a popular optimal MAPF algorithm, and provides it with the ability to identify and reason with symmetries in MAPF. While existing work identifies a limited form of symmetries using rectangle reasoning and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH with rectangle reasoning, a state-of-the-art variant of CBS, with respect to runtime and success rate.

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(last updated in 2022)