论战略性多智能体机器人团队的协调效率
On the coordination efficiency of strategic multi-agent robotic teams
AI总结：
研究随机环境下多智能体去中心化协调问题，提出协调效率概率度量并给出信息论上界，发现纳什均衡策略在协调效率与期望效用间存在非平凡权衡。
摘要：
我们研究在随机环境中，一组战略性决策者选择参与或不参与任务以实现去中心化协调的问题。首先，我们定义了一类对称效用博弈，它涵盖了一大类协调博弈，包括被称为全局博弈的流行框架。为了研究参与随机协调博弈的智能体实际协调的程度，我们提出了一种新的协调效率的概率度量。然后，我们提供了一个关于协调效率的通用信息论上界，作为观测信道中噪声量的函数。最后，我们重新审视了一大类全局博弈，并说明它们的纳什均衡策略可能不如确定性等价策略具有协调效率，尽管它们提供了更好的期望效用。这一反直觉的结果确立了协调博弈中协调效率与期望效用之间的非平凡权衡。
英文摘要：
We study the problem of achieving decentralized coordination by a group of strategic decision makers choosing to engage or not in a task in a stochastic setting. First, we define a class of symmetric utility games that encompass a broad class of coordination games, including the popular framework known as \textit{global games}. With the goal of studying the extent to which agents engaging in a stochastic coordination game indeed coordinate, we propose a new probabilistic measure of coordination efficiency. Then, we provide an universal information theoretic upper bound on the coordination efficiency as a function of the amount of noise in the observation channels. Finally, we revisit a large class of global games, and we illustrate that their Nash equilibrium policies may be less coordination efficient then certainty equivalent policies, despite of them providing better expected utility. This counter-intuitive result, establishes the existence of a nontrivial trade-offs between coordination efficiency and expected utility in coordination games.
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