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Evolution of Strategies With Different Representation Schemes in a Spatial Iterated Prisoner's Dilemma Game

机译:空间迭代囚徒困境游戏中具有不同表示方案的策略的演变

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The iterated prisoner's dilemma (IPD) game has been frequently used to examine the evolution of cooperative behavior among agents in the field of evolutionary computation. It has been demonstrated that various factors are related to the evolution of cooperative behavior. One well-known factor is spatial relations among agents. The IPD game is often played in a 2-D grid world. Such a spatial IPD game has a neighborhood structure, which is used to choose opponents for the IPD game and parents for genetic operations. Another important factor is the choice of a representation scheme to encode the strategy of each agent. Different representation schemes often lead to different results. Whereas the choice of a representation scheme is known to be important, a mixture of different representation schemes has not been examined for the spatial IPD game in the literature. That is, a population of homogeneous agents with the same representation scheme has been usually assumed in the literature. In this paper, we introduce the use of different representation schemes in a single population to the spatial IPD game in order to examine the evolution of cooperative behavior under more general assumptions. With the use of different representation schemes, we can examine the evolution of cooperative behavior in various settings such as partial interaction through the IPD game, partial interaction through crossover, full interaction through the IPD game and crossover, and no interaction between different subpopulations of agents.
机译:迭代囚徒困境(IPD)博弈经常被用来检验进化计算领域中特工之间合作行为的演变。已经证明各种因素与合作行为的发展有关。一个众所周知的因素是代理之间的空间关系。 IPD游戏通常在二维网格世界中玩。这样的空间IPD游戏具有邻域结构,用于选择IPD游戏的对手和父母进行基因操作。另一个重要因素是选择表示方案来编码每个代理的策略。不同的表示方案通常会导致不同的结果。尽管已知代表方案的选择很重要,但文献中尚未对空间IPD游戏研究过不同代表方案的混合。也就是说,文献中通常假设具有相同表示方案的同类试剂。在本文中,我们将在单个种群中使用不同的表示方案引入空间IPD博弈中,以便在更笼统的假设下研究合作行为的演变。通过使用不同的表示方案,我们可以检查各种环境下合作行为的演变,例如通过IPD游戏进行部分交互,通过交叉进行部分交互,通过IPD游戏和交叉进行完全交互以及不同代理子群体之间没有交互。

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