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.

互斥传播是一种有效的约束传播形式，广泛用于 AI 规划中，以紧密逼近给定状态的可达状态。我们在多代理路径查找（MAPF）的背景下利用了这个想法。当适应 MAPF 时，互斥体传播为 CBS（一种流行的基于最优搜索的 MAPF 算法）以及 MDD-SAT（一种基于最优可满足性的 MAPF 算法）中的冲突解决提供了更强的约束。互斥传播为 CBS 提供了打破 MAPF 中的对称性的能力，并为 MDD-SAT 提供了比单位传播进行更强推理的能力。虽然现有工作确定了有限的对称形式并需要手动设计对称破坏约束，但互斥体传播更通用，并允许自动设计对称破坏约束。我们的实验结果表明，具有互斥体传播的 CBS 在成功率方面能够胜过 CBS 的最新变体 CBSH-RCT。我们还表明，在成功率方面，具有互斥传播的 MDD-SAT 通常比 MDD-SAT 表现更好。