We study dynamic multi-robot task allocation under uncertain task completion, time-window constraints, and incomplete information. Tasks arrive online over a finite horizon and must be completed within specified deadlines, while agents operate from distributed hubs with limited sensing and communication. We model incomplete information through hub-based sensing regions that determine task visibility and a communication graph that governs inter-hub information exchange. Using this framework, we propose Iterative Best Response (IBR), a decentralized policy in which each agent selects the task that maximizes its marginal contribution to the locally observed welfare. We compare IBR against three baselines: Earliest Due Date first (EDD), Hungarian algorithm, and Stochastic Conflict-Based Allocation (SCoBA), on a city-scale package-delivery domain with up to 100 drones and varying task arrival scenarios. Under full and sparse communication, IBR achieves competitive task-completion performance with lower computation time.

翻译：我们研究在任务完成不确定性、时间窗口约束及不完全信息下的动态多机器人任务分配问题。任务在有限时间范围内在线到达，并需在指定截止日期前完成，而智能体从分布式枢纽出发且传感与通信能力有限。我们通过基于枢纽的感知区域（决定任务可视性）和控制枢纽间信息交换的通信图来建模不完全信息。基于该框架，我们提出迭代最优响应（IBR），这是一种去中心化策略，其中每个智能体选择能最大化其对局部观察福利的边际贡献的任务。我们在一个城市级包裹配送场景（包含多达100架无人机及不同任务到达场景）中，将IBR与三种基线方法——最早截止时间优先（EDD）、匈牙利算法及随机冲突分配（SCoBA）——进行对比。在完全通信和稀疏通信条件下，IBR在任务完成性能上具有竞争力，同时计算时间更短。