We consider a Multi-Agent Path Finding (MAPF) setting where agents have been assigned a plan, but during its execution some agents are delayed. Instead of replanning from scratch when such a delay occurs, we propose delay introduction, whereby we delay some additional agents so that the remainder of the plan can be executed safely. We show that the corresponding decision problem is NP-Complete in general. However, in practice we can find optimal delay-introductions using CBS for very large numbers of agents, and both planning time and the resulting length of the plan are comparable, and sometimes outperform, the state-of-the-art heuristics for replanning. We also examine the benefits of our method from an explainability point of view.

翻译：我们考虑多智能体路径规划（MAPF）场景：智能体已分配规划方案，但在执行过程中部分智能体出现延迟。针对此类延迟问题，我们提出延迟引入方法——通过主动延迟部分其他智能体，使剩余规划方案得以安全执行。研究表明，该决策问题在一般情况下是NP完全的。然而在实际应用中，我们能够使用CBS算法为大规模智能体寻找最优延迟引入方案，其规划时间与生成的路径长度均与当前最先进的重规划启发式算法相当，部分情况下甚至表现更优。此外，我们还从可解释性角度探讨了该方法带来的优势。