To study open-ended coevolution, we define a complexity metric over interacting finite state machines playing formal language prediction games, and study the dynamics of populations under competitive and cooperative interactions. In the past purely competitive and purely cooperative interactions have been studied extensively, but neither can successfully and continuously drive an arms race. We present quantitative results using this complexity metric and analyze the causes of varying rates of complexity growth across different types of interactions. We find that while both purely competitive and purely cooperative coevolution are able to drive complexity growth above the rate of genetic drift, mixed systems with both competitive and cooperative interactions achieve significantly higher evolved complexity.

中文翻译：

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预测游戏中不断发展的复杂性

为了研究开放式协同进化，我们定义了在玩正式语言预测游戏的交互有限状态机上的复杂性度量，并研究了竞争和合作交互下的种群动态。过去，纯粹竞争和纯粹合作的互动已经被广泛研究，但都不能成功和持续地推动军备竞赛。我们使用此复杂性指标呈现定量结果，并分析不同类型交互中复杂性增长速度不同的原因。我们发现，虽然纯粹竞争和纯粹合作的共同进化都能够推动复杂性增长超过遗传漂移的速度，但具有竞争性和合作性相互作用的混合系统实现了显着更高的进化复杂性。