基于积雪漂移进化博弈模型的多智能体合作:觅食任务案例研究

Ahmad Esmaeili, Zahra Ghorrati, E. Matson
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引用次数: 6

摘要

协作通常被认为是多智能体系统区别于其他相关领域(如分布式计算)的关键和不明确的概念之一。验证各种协作算法有效性的常用基准之一是多智能体觅食任务。人们提出了不同的方法,其中基于马尔可夫博弈的方法被广泛使用,但它们不能为群体选择一致的均衡。本文提出了一种基于进化博弈的算法。该方法将智能体之间的相互作用建模为雪花漂移博弈,以演化出进化稳定策略(ESS),为智能体群体带来最大的回报。仿真结果验证了该算法的有效性。
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Multi-agent Cooperation Using Snow-Drift Evolutionary Game Model: Case Study in Foraging Task
Cooperation is often considered as one of the key and unclear concepts, which differentiates multi-agent systems from other related fields such as distributed computing. One of the popular benchmarks for the verification of the effectiveness of various cooperation algorithms is multi-agent foraging task. Different approaches have been proposed among which Markov game based ones are widely used, though they could not select consistent equilibrium for the group. In this paper, an evolutionary game based method is proposed. In this method, the interactions among the agents are modeled by snow-drift game to evolve the evolutionary stable strategy (ESS) and bring the maximal reward for the group of agents. The simulation verified the efficiency of the proposed algorithm.
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