迭代囚徒困境的多智能体遗传网络图的检验

J. A. Brown
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引用次数: 4

摘要

多智能体遗传网络(multi Agent Genetic Networks, MAGnet)是一种空间结构的进化算法,它既移动正在进化的智能体,也移动关于组合图的问题实例。之前的工作已经研究了它们在迭代囚徒困境(一个著名的非零和博弈)中的应用,以便根据行为对代理类型进行分类。只检查了一个小的完全图。在这项研究中,一个更大的32个节点的图集被检查。所检查的图是:一个循环图,两个具有不同内环的Peterson图,一个五维超立方体和完整图。这些图和属性被用于许多典型的代理,以及一些涉及握手的有趣类型。研究发现,当图内的连通性较高时,MAGnet系统产生的分类与较小的图相似。低图连通性导致形成不相交子图的过程;这是基于导致子种群崩溃的进化方法,在这种方法中,节点上的问题数量趋于零,节点被删除。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Examination of graphs in Multiple Agent Genetic Networks for Iterated Prisoner's Dilemma
Multiple Agent Genetic Networks (MAGnet) are spatially structured evolutionary algorithms which move both evolving agents as well as instances of a problem about a combinatorial graph. Previous work has examined their use on the Iterated Prisoner's Dilemma, a well known non-zero sum game, in order for classification of agent types based on behaviours. Only a small complete graph was examined. In this study, a larger set of graphs with thirty-two nodes are examined. The graphs examined are: a cycle graph, two Peterson graphs with differing internal rings, a hypercube in five dimensions, and the complete graph. These graphs and properties are examined for a number of canonical agents, as well as a few interesting types which involve handshaking. It was found that the MAGnet system produces a similar classification as the smaller graph when the connectivity within the graph is high. Lower graph connectivity leads to a process by which disjoint subgraphs can be formed; this is based on the method of evolution causing a subpopulation collapse in which the number of problems on a node tends to zero and the node is removed.
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