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Enumerating Connected Subgraphs and Computing the Myerson and Shapley Values in Graph-Restricted Games

机译:枚举连通子图并计算图受限游戏中的Myerson和Shapley值

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At the heart of multi-agent systems is the ability to cooperate to improve the performance of individual agents and/or the system as a whole. While a widespread assumption in the literature is that such cooperation is essentially unrestricted, in many realistic settings this assumption does not hold. A highly influential approach for modelling such scenarios are graph-restricted games introduced by Myerson [36]. In this approach, agents are represented by nodes in a graph, edges represent communication channels, and a group can generate an arbitrary value only if there exists a direct or indirect communication channel between every pair of agents within the group. Two fundamental solution-concepts that were proposed for such games are the Myerson value and the Shapley value. While an algorithm has been developed to compute the Shapley value in arbitrary graph-restricted games, no such general-purpose algorithm has been developed for the Myerson value to date. With this in mind, we set out to develop for such games a general-purpose algorithm to compute the Myerson value, and a more efficient algorithm to compute the Shapley value. Since the computation of either value involves enumerating all connected induced subgraphs of the game's underlying graph, we start by developing an algorithm dedicated to this enumeration, and then we show empirically that it is faster than the state of the art in the literature. Finally, we present a sample application of both algorithms, in which we test the Myerson value and the Shapley value as advanced measures of node centrality in networks.
机译:多代理系统的核心是协作以提高单个代理和/或整个系统的性能的能力。尽管在文献中普遍假设这种合作基本上不受限制,但在许多现实情况下,这种假设并不成立。 Myerson [36]提出了一种图形化限制游戏,对这种情况进行建模非常有影响力。在这种方法中,代理由图形中的节点表示,边表示通信通道,并且仅当组内每对代理之间存在直接或间接通信通道时,组才可以生成任意值。针对此类游戏提出的两个基本解决方案概念是Myerson值和Shapley值。尽管已经开发出一种算法来计算任意图限制游戏中的Shapley值,但迄今为止,还没有针对Myerson值开发出这种通用算法。考虑到这一点,我们着手为此类游戏开发一种通用算法来计算Myerson值,以及一种更高效的算法来计算Shapley值。由于任一值的计算都涉及枚举游戏基础图的所有连接的诱导子图,因此我们从开发专用于该枚举的算法开始,然后从经验上证明它比文献中的现有技术要快。最后,我们提出了这两种算法的示例应用程序,其中我们测试了Myerson值和Shapley值作为网络中节点中心性的高级度量。

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