Multiple Opponent Optimization of Prisoner’s Dilemma Playing Agents

D. Ashlock, J. A. Brown, P. Hingston
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引用次数: 8

Abstract

Agents for playing iterated prisoner's dilemma are commonly trained using a coevolutionary system in which a player's score against a selection of other members of an evolving population forms the fitness function. In this study we examine instead a version of evolutionary iterated prisoner's dilemma in which an agent's fitness is measured as the average score it obtains against a fixed panel of opponents called an examination board. The performance of agents trained using examination boards is compared against agents trained in the usual coevolutionary fashion. This includes assessing the relative competitive ability of players evolved with evolution and coevolution. The difficulty of several experimental boards as optimization problems is compared. A number of new types of strategies are introduced. These include sugar strategies which can be exploited with some difficulty and treasure hunt strategies which have multiple trapping states with different levels of exploitability. The degree to which strategies trained with different examination boards produce different agents is investigated using fingerprints.
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囚徒困境博弈主体的多对手优化
玩迭代囚徒困境的代理通常使用一种共同进化系统进行训练,在这种系统中,玩家对进化群体中其他成员的选择得分形成适应度函数。在这项研究中,我们研究了进化迭代囚徒困境的一个版本,在这个版本中,一个主体的适合度是用它在一个被称为考试委员会的固定对手小组中获得的平均分来衡量的。使用考试板训练的代理的性能与通常的共同进化方式训练的代理进行比较。这包括评估随着进化和共同进化而进化的玩家的相对竞争能力。比较了几种实验板作为优化问题的难度。介绍了一些新的策略类型。其中包括具有一定难度的糖策略和具有不同可利用程度的多个陷阱状态的寻宝策略。使用指纹调查了不同考试委员会训练的策略产生不同代理的程度。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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