Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not result in an efficient plan, and sometimes cannot even yield a feasible plan. On the other hand, replanning other agents may lead to a cascade of changes and delays. We show how to efficiently replan by tracking and using the temporal flexibility of other agents while avoiding cascading delays. This flexibility is the maximum delay an agent can take without changing the order of or further delaying more agents. Our algorithm, FlexSIPP, precomputes all possible plans for the delayed agent, also returning the changes for the other agents, for any single-agent delay within the given scenario. We demonstrate our method in a real-world case study of replanning trains in the densely-used Dutch railway network. Our experiments show that FlexSIPP provides effective solutions, relevant to real-world adjustments, and within a reasonable timeframe.

翻译：执行多智能体规划时，若某一智能体发生延迟，通常会导致与其他智能体产生冲突，这使得规划执行面临挑战。因此，我们需要快速找到新的安全规划。仅对延迟智能体进行重规划往往无法得到高效方案，有时甚至无法获得可行解。另一方面，对其他智能体进行重规划可能导致连锁性的变更与延误。本文通过追踪并利用其他智能体的时间灵活性，同时避免连锁延误，展示了如何进行高效重规划。这种灵活性是指智能体在不改变其他智能体顺序或引发进一步延误的前提下所能承受的最大延迟。我们提出的FlexSIPP算法能够为给定场景中任何单智能体延迟，预计算延迟智能体的所有可能规划方案，同时返回其他智能体所需的调整。我们在实际案例中演示了该方法：对高负荷运行的荷兰铁路网络中的列车进行重规划。实验表明，FlexSIPP能在合理时间内提供与现实调整需求相关的有效解决方案。