We consider an Anonymous Multi-Agent Path-Finding (AMAPF) problem where the set of agents is confined to a graph, a set of goal vertices is given and each of these vertices has to be reached by some agent. The problem is to find an assignment of the goals to the agents as well as the collision-free paths, and we are interested in finding the solution with the optimal makespan. A well-established approach to solve this problem is to reduce it to a special type of a graph search problem, i.e. to the problem of finding a maximum flow on an auxiliary graph induced by the input one. The size of the former graph may be very large and the search on it may become a bottleneck. To this end, we suggest a specific search algorithm that leverages the idea of exploring the search space not through considering separate search states but rather bulks of them simultaneously. That is, we implicitly compress, store and expand bulks of the search states as single states, which results in high reduction in runtime and memory. Empirically, the resultant AMAPF solver demonstrates superior performance compared to the state-of-the-art competitor and is able to solve all publicly available MAPF instances from the well-known MovingAI benchmark in less than 30 seconds.

翻译：我们考虑一个匿名多智能体路径规划（Anonymous Multi-Agent Path-Finding, AMAPF）问题：智能体集合被限制在一个图中，给定一组目标顶点，每个目标顶点需由某个智能体到达。问题的目标是寻找目标与智能体的分配方案以及无碰撞路径，并致力于得到最优完工时间的解。解决该问题的一种成熟方法是将它约简为一种特殊类型的图搜索问题，即在由输入图导出的辅助图上寻找最大流的问题。前一张图的规模可能非常大，对其进行搜索可能成为瓶颈。为此，我们提出一种特定的搜索算法，其核心思想是并非通过逐一考虑独立搜索状态，而是同时批量处理这些状态来探索搜索空间。也就是说，我们将搜索状态的聚合体隐式地压缩、存储并扩展为单一状态，从而大幅降低运行时间和内存消耗。实证结果表明，该算法生成的AMAPF求解器在性能上优于现有最先进竞品，并且能够在30秒内求解所有来自知名MovingAI基准测试的公开MAPF实例。