Abstract

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 CBS, a popular optimal search-based MAPF algorithm, as well as in MDD-SAT, an optimal satisfiability-based MAPF algorithm. Mutex propagation provides CBS with the ability to break symmetries in MAPF and provides MDD-SAT with the ability to make stronger inferences than unit propagation. While existing work identifies a limited form of symmetries 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-RCT, a state-of-the-art variant of CBS, with respect to the success rate. We also show that MDD-SAT with mutex propagation often performs better than MDD-SAT with respect to the success rate.

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