论文标题
使用随机机器的自发展合作策略的出现和稳定性
Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines
论文作者
论文摘要
为了研究合作行为的起源，我们开发了一个顺序策略的进化模型，并通过计算机模拟测试了我们的模型。随机机器代表的顺序策略通过迭代的囚犯困境（IPD）与其他人群中的其他代理商进行了评估，从而使共同进化。我们通过提出一种新型机制来突变随机的摩尔机器，从而扩展了过去的作品，该机制使较富裕的机器能够进化。然后对这些机器进行各种选择机制，并分析所得的进化策略。我们发现，合作确实可以自发地出现在不断发展的迭代PD的人群中，特别是以触发策略的形式出现。此外，我们发现所产生的种群融合到进化稳定的状态，并且对突变具有弹性。为了测试我们提出的突变机制和仿真方法的普遍性，我们还将机器演变为玩其他游戏，例如鸡肉，雄鹿狩猎和战斗，并获得了在NASH平衡中表现出色的策略。
To investigate the origin of cooperative behaviors, we developed an evolutionary model of sequential strategies and tested our model with computer simulations. The sequential strategies represented by stochastic machines were evaluated through games of Iterated Prisoner's Dilemma (IPD) with other agents in the population, allowing co-evolution to occur. We expanded upon past works by proposing a novel mechanism to mutate stochastic Moore machines that enables a richer class of machines to be evolved. These machines were then subjected to various selection mechanisms and the resulting evolved strategies were analyzed. We found that cooperation can indeed emerge spontaneously in evolving populations playing iterated PD, specifically in the form of trigger strategies. In addition, we found that the resulting populations converged to evolutionarily stable states and were resilient towards mutation. In order to test the generalizability of our proposed mutation mechanism and simulation approach, we also evolved the machines to play other games such as Chicken, Stag Hunt, and Battle, and obtained strategies that perform as well as mixed strategies in Nash Equilibrium.
