We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the end of the operation horizon. We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination, and addresses them in a hierarchical manner. The lower layer computes policies for individual agents using dynamic programming with tree search, and the upper layer resolves conflicts in individual plans to obtain a valid multi-agent allocation. Our algorithm, Stochastic Conflict-Based Allocation (SCoBA), is optimal in expectation and complete under some reasonable assumptions. In practice, SCoBA is computationally efficient enough to interleave planning and execution online. On the metric of successful task completion, SCoBA consistently outperforms a number of baseline methods and shows strong competitive performance against an oracle with complete lookahead. It also scales well with the number of tasks and agents. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.

我们考虑在时间窗口约束和任务完成不确定性下将任务动态分配给多个代理的问题。我们的目标是在操作范围结束时最小化不成功任务的数量。我们提出了一种多机器人分配算法，该算法将不确定性和多代理协调下的顺序决策的关键计算挑战解耦，并以分层方式解决它们。下层使用动态规划和树搜索计算单个代理的策略，上层解决单个计划中的冲突以获得有效的多代理分配。我们的算法，基于随机冲突的分配(SCoBA)，在一些合理的假设下是最优的并且是完整的。在实践中，SCoBA 的计算效率足以在线交叉规划和执行。在成功完成任务的指标上，SCoBA 始终优于许多基线方法，并显示出与具有完全前瞻性的预言机相比的强大竞争性能。它还可以很好地扩展任务和代理的数量。我们在两个不同领域的广泛模拟中验证了我们的结果：城市中的多臂传送带取放和多无人机交付调度。