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.

翻译：我们考虑多智能体路径规划（MAPF）场景，其中每个智能体已被分配规划方案，但在执行过程中部分智能体出现延迟。我们提出延迟引入方法，并非在发生延迟时重新从头规划，而是额外延迟若干智能体，以确保剩余规划部分能够安全执行。我们证明相应的决策问题通常为NP完全问题。然而，在实践中，我们可通过基于冲突的搜索（CBS）算法，对大规模智能体群体找到最优延迟引入策略；其规划时间与规划结果长度均与当前最先进的重新规划启发式方法相当，有时甚至更优。