超越多智能体寻径中的两两推理

Bojie Shen, Zhe Chen, Jiaoyang Li, M. A. Cheema, Daniel D. Harabor, P. J. Stuckey
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引用次数: 1

摘要

在多智能体寻径(MAPF)中,我们被要求为移动智能体团队规划无冲突的路径。最优MAPF的主要方法是基于冲突的搜索(CBS),这是近年来受到广泛关注的一个算法家族,其效率和有效性已经有了很大的进步。然而,CBS最近的所有收益都来自于对代理人的推理。在本文中,我们展示了如何通过同时对两个以上的代理进行推理来进一步改进CBS。我们的新聚类推理技术允许我们为CBS生成更强的边界,并识别更多的旁路(替代成本等效路径),从而减少CBS冲突树中的节点数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond Pairwise Reasoning in Multi-Agent Path Finding
In Multi-Agent Path Finding (MAPF), we are asked to plan collision-free paths for teams of moving agents. Among the leading methods for optimal MAPF is Conflict-Based Search (CBS), an algorithmic family which has received intense attention in recent years and for which large advancements in efficiency and effectiveness have been reported. Yet all of the recent CBS gains come from reasoning over pairs of agents only. In this paper, we show how to further improve CBS by reasoning about more than two agents at the same time. Our new cluster reasoning techniques allow us to generate stronger bounds for CBS and to identify more bypasses (alternative cost-equivalent paths), which reduce the number of nodes in the CBS conflict tree.
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