Xue Lei, Cao Xianghui
IEEE Trans Neural Netw Learn Syst. 2019 Dec;30(12):3656-3664. doi: 10.1109/TNNLS.2019.2900592. Epub 2019 Mar 21.
Multiagent systems (MASs) are usually applied with agents classified into leaders and followers, where selecting appropriate leaders is an important issue for formation control applications. In this paper, we investigate two leader selection problems in second-order MAS, namely, the problem of choosing up to a given number of leaders to minimize the formation error and the problem of choosing the minimum number of leaders to achieve a tolerated level of error. We propose a game theoretical method to address them. Specifically, we design a supermodular game for the leader selection problems and theoretically prove its supermodularity. In order to reach Nash equilibrium of the game, we propose strategies for the agents to learn to select leaders based on stochastic fictitious play. Extensive simulation results demonstrate that our method outperforms existing ones.
多智能体系统（MASs）通常将智能体分为领导者和跟随者来应用，其中选择合适的领导者是编队控制应用中的一个重要问题。在本文中，我们研究二阶多智能体系统中的两个领导者选择问题，即选择至多给定数量的领导者以使编队误差最小化的问题，以及选择最小数量的领导者以达到可容忍误差水平的问题。我们提出一种博弈论方法来解决这些问题。具体而言，我们为领导者选择问题设计了一个超模博弈，并从理论上证明了其超模性。为了达到博弈的纳什均衡，我们提出智能体基于随机虚拟博弈来学习选择领导者的策略。大量仿真结果表明，我们的方法优于现有方法。
