标题： 具有部分未知奖励的自利代理人的分布式任务分配 显示英文标题

标题： Distributed Task Allocation for Self-Interested Agents with Partially Unknown Rewards

摘要： 本文提供了一种解决任务分配问题的新方案，通过该方案，一组代理以分布式方式决定对离散任务集的分配。 在此设定中，异构代理对每个任务有个人偏好和相关奖励；然而，这些奖励仅在渐近意义上可知。 我们首先通过组合划分博弈来表述已知奖励的任务分配问题，对每个代理的任务数量没有约束。 我们将此问题放松为一个权重博弈，结合前者，证明其在相应的纳什均衡（NE）集合中包含最优任务分配。 然后我们提出一种投影、最佳响应、上升梯度动力学（PBRAG），该动力学在有限时间内收敛到一个纳什均衡。 这构成了一个分布式在线版本的基础，可以通过一个协议子程序处理逐渐收敛的奖励序列。 我们展示了支持我们结果的仿真。 显示英文摘要

摘要： This paper provides a novel solution to a task allocation problem, by which a group of agents decides on the assignment of a discrete set of tasks in a distributed manner. In this setting, heterogeneous agents have individual preferences and associated rewards for doing each task; however, these rewards are only known asymptotically. We start by formulating the assignment problem by means of a combinatorial partition game for known rewards, with no constraints on number of tasks per agent. We relax this into a weight game, which together with the former, are shown to contain the optimal task allocation in the corresponding set of Nash Equilibria (NE). We then propose a projected, best-response, ascending gradient dynamics (PBRAG) that converges to a NE in finite time. This forms the basis of a distributed online version that can deal with a converging sequence of rewards by means of an agreement sub-routine. We present simulations that support our results