循序渐进的多边寻求共同目标

Igor Rochlin, David Sarne, G. Zussman
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引用次数: 9

摘要

受动态频谱接入网络应用的启发,我们重点研究了一个系统,在这个系统中,几个智能体进行昂贵的单个搜索,每个智能体的利益是根据最小的获得值来确定的。这种搜索模式适用于许多系统,包括运输和旅行计划。本文正式介绍并分析了通用模型的一个序列变体。根据该变体,在任何给定时间只有单个代理进行搜索,并且当一个代理启动其搜索时,它拥有关于其他代理迄今为止获得的最小值的完整信息。根据所得到的Stackelberg博弈的均衡性,我们证明了每个智能体的搜索策略是基于保留值的,并展示了如何计算保留值。我们还分析了agent完全合作时(即以期望共同利益最大化为目标时)的最优搜索策略。利用一个合成的同构环境来说明各智能体的均衡策略和期望收益,从而展示了这种新搜索方案的特性和合作的收益。
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Sequential Multilateral Search for a Common Goal
Motivated by applications in Dynamic Spectrum Access Networks, we focus on a system in which a few agents are engaged in a costly individual search where each agent's benefit is determined according to the minimum obtained value. Such a search pattern is applicable to many systems, including shipment and travel planning. This paper formally introduces and analyzes a sequential variant of the general model. According to that variant, only a single agent searches at any given time, and when an agent initiates its search, it has complete information about the minimum value obtained by the other agents so far. We prove that the search strategy of each agent, according to the equilibrium of the resulting Stackelberg game, is reservation-value based, and show how the reservation values can be calculated. We also analyze the agents' optimal search strategies when they are fully cooperative (i.e., when they aim to maximize the expected joint benefit). The equilibrium strategies and the expected benefit of each agent are illustrated using a synthetic homogeneous environment, thereby demonstrating the properties of this new search scheme and the benefits of cooperation.
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