A coalition structure generation algorithm based on partition cardinality structure

Hai-Yan Wu, Shan-li Hu
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引用次数: 3

Abstract

Coalition formation is a key topic on multi-agent systems. As the number of coalition structures is large, it is not allowed to search the whole coalition structure tree exhaustively. So, a partial search which is guaranteed to be within a bound from optimum is interested. A large number of previous work has been done to find the optimal coalition structure in Characteristic Function Games (CFGs), where the value of a coalition was independent from co-existing coalitions in the system. However, usually it is not appropriate to the real situation because an agent's effectiveness may change from one coalition structure to another. Thus, much more general Partition Function Games (PFGs) have to be applied. In this context, we develop an algorithm based on partition cardinality structure, which consider the coalition value in PFGs. We use different strategies in two classes of PFGs, and improve the bound with further search.
一种基于分区基数结构的联盟结构生成算法
联盟的形成是多智能体系统研究的一个关键问题。由于联盟结构的数量很大,不允许对整个联盟结构树进行穷举搜索。因此,我们感兴趣的是保证在最优区间内的部分搜索。在特征函数博弈(CFGs)中,联盟的价值独立于系统中共存的联盟,前人已经做了大量的工作来寻找最优联盟结构。然而,通常情况下,它并不适合于实际情况,因为代理的有效性可能会随着联盟结构的不同而变化。因此,我们必须应用更普遍的分割函数游戏(pfg)。在此背景下,我们开发了一种基于分区基数结构的算法,该算法考虑了pfg中的联盟值。我们对两类PFGs采用了不同的策略,并通过进一步的搜索改进了界。
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