Multi-Agent Path Finding (MAPF) is the problem of planning paths for agents to reach their targets from their start locations, such that the agents do not collide while executing the plan. In many settings, the plan (or a digest thereof) is conveyed to a supervising entity, e.g., for confirmation before execution, for a report, etc. In such cases, we wish to convey that the plan is collision-free with minimal amount of information. To this end, we propose an for MAPF. The scheme decomposes a plan into segments such that within each segment, the agents' paths are disjoint. We can then convey the plan whilst convincing that it is collision-free, using a small number of frames (dubbed an ). We can also measure the simplicity of a plan by the number of segments required for the decomposition. We study the complexity of algorithmic problems that arise by the explanation scheme and the tradeoff between the length (makespan) of a plan and its minimal decomposition. We also introduce two centralized (i.e. runs on a single CPU with full knowledge of the multi-agent system) algorithms for planning with explanations. One is based on a coupled search algorithm similar to A, and the other is a decoupled method based on Conflict-Based Search (CBS). We refer to the latter as (XG-CBS), which uses a low-level search for individual agents and maintains a high-level conflict tree to guide the low-level search to avoid collisions as well as increasing the number of segments. We propose four approaches to the low-level search of XG-CBS by modifying A for explanations and analyze their effects on the completeness of XG-CBS. Finally, we highlight important aspects of the proposed explanation scheme in various MAPF problems and empirically evaluate the performance of the proposed planning algorithms in a series of benchmark problems.

中文翻译：

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多智能体路径查找中的时间分割及其可解释性应用

多智能体路径查找（MAPF）是规划智能体从起始位置到达目标的路径的问题，使得智能体在执行计划时不会发生碰撞。在许多情况下，计划（或其摘要）被传达给监督实体，例如，用于执行前的确认、报告等。在这种情况下，我们希望以最小的量传达该计划是无冲突的的信息。为此，我们建议设立 MAPF。该方案将计划分解为多个片段，使得在每个片段内，代理的路径是不相交的。然后，我们可以使用少量的帧（称为 ）传达该计划，同时确信它是无碰撞的。我们还可以通过分解所需的段数来衡量计划的简单性。我们研究由解释方案产生的算法问题的复杂性以及计划的长度（makespan）与其最小分解之间的权衡。我们还介绍了两种集中式（即在充分了解多智能体系统的单个 CPU 上运行）算法进行规划并进行解释。一种是基于类似于A的耦合搜索算法，另一种是基于冲突搜索（CBS）的解耦方法。我们将后者称为（XG-CBS），它对个体代理使用低级搜索，并维护一个高级冲突树来指导低级搜索以避免冲突以及增加分段数量。我们通过修改 A 进行解释，提出了 XG-CBS 低级搜索的四种方法，并分析了它们对 XG-CBS 完整性的影响。最后，我们强调了所提出的解释方案在各种 MAPF 问题中的重要方面，并根据经验评估了所提出的规划算法在一系列基准问题中的性能。