研究人员开发了一种新方法，通过将其重新表述为一种特定的多边际最优传输（MMOT）问题来解决多智能体路径查找（MAPF）问题。该方法利用马尔可夫结构将MMOT的计算复杂度降低到多项式大小的线性规划。对于大规模应用，该方法通过薛定谔桥进一步调整，薛定谔桥提供了一种迭代的、Sinkhorn类型的解决方案，在保持近乎最优结果的同时显著降低了复杂度。 AI
影响 引入了一种更高效的多机器人协调方法，可能对物流和自主系统产生影响。
排序理由 该集群包含一篇详细介绍解决复杂计算问题新方法的学术论文。
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We consider anonymous multi-agent path finding (MAPF) where a set of robots is tasked to travel to a set of targets on a finite, connected graph. We show that MAPF can be cast as a special class of multi-marginal optimal transport (MMOT) problems with an underlying Markovian stru…
We consider anonymous multi-agent path finding (MAPF) where a set of robots is tasked to travel to a set of targets on a finite, connected graph. We show that MAPF can be cast as a special class of multi-marginal optimal transport (MMOT) problems with an underlying Markovian stru…