A large series of studies on evolving agents to play mathematical games has demonstrated that many factors can significantly impact which agents arise, when those agents arise during evolution, and how robust they are in their play against other agents. Some or all of these factors have been shown to be relevant in the iterated prisoner's dilemma, the snowdrift game, and a fairly complex game called divide-the-dollar. This study demonstrates the impact or representation and agent resource allocation for a new game called coordination prisoner's dilemma. This paper demonstrates protocols from a recently published book for analysis of agent behavior and extends the work to another game, the first three-move game so treated. A new representation for agents playing mathematical games is introduced, a linear genetic programming register machine. New metrics for agent behavior including total exploitation, strategic variability, and action entropy are introduced. It is found that varying the representation and resource levels within a representation changes the types of game playing agents produced by evolution for coordination prisoner's dilemma.

中文翻译：

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测试用于表征通过新游戏进化训练的游戏代理的协议

大量关于进化智能体玩数学游戏的研究表明，许多因素可以显着影响哪些智能体出现，这些智能体在进化过程中何时出现，以及它们在对抗其他智能体的游戏中有多强大。这些因素中的部分或全部已被证明与重复的囚徒困境、雪堆游戏和称为分钱游戏的相当复杂的游戏有关。这项研究展示了一种称为协调囚徒困境的新博弈的影响或代表和代理资源分配。这篇论文展示了最近出版的一本书中的协议，用于分析代理行为，并将工作扩展到另一个游戏，第一个这样处理的三步游戏。介绍了一种用于玩数学游戏的代理的新表示，一种线性遗传编程注册机。引入了代理行为的新指标，包括总开发、战略可变性和行动熵。发现在一个表示中改变表示和资源水平会改变由协调囚徒困境进化产生的博弈代理的类型。