Continuous response threshold functions to coordinate collaborative tasks in multi-agent systems are commonly employed models in a number of fields including ethology, economics, and swarm robotics. Although empirical evidence exists for the response threshold model in predicting and matching swarm behavior for social insects, there has been no formal argument as to why natural swarms use this approach and why it should be used for engineering artificial ones. In this paper, we show, by formulating task allocation as a global game, that continuous response threshold functions used for communication-free task assignment result in system level Bayesian Nash equilibria. Building up on these results, we show that individual agents not only do not need to communicate with each other, but also do not need to model each other’s behavior, which makes this coordination mechanism accessible to very simple agents, suggesting a reason for their prevalence in nature and motivating their use in an engineering context.

中文翻译：

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将有限信息下的多机器人任务分配建模为全局游戏

连续响应阈值功能（用于协调多主体系统中的协作任务）是许多领域（包括行为学，经济学和群体机器人技术）中常用的模型。尽管存在关于响应阈值模型预测和匹配社交昆虫群体行为的经验证据，但是对于为什么自然群体使用这种方法以及为什么应将其用于工程人工方法尚无正式论点。在本文中，我们通过将任务分配公式化为全局博弈，证明了用于无通信任务分配的连续响应阈值函数会导致系统级贝叶斯纳什均衡。在这些结果的基础上，我们表明个体代理不仅不需要相互交流，而且还不需要为彼此的行为建模，