Exploiting coalition in co-evolutionary learning

Yeon-Gyu Seo, Sung-Bae Cho, Xin Yao

Abstract: Adaptive behaviors often emerge through interactions between adjacent neighbors in dynamic systems, such as social and economic systems. In many cases, an individual's behavior can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the iterated prisoner's dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model a dynamic system such as social or economic systems. We investigate coalitions consisting of many players and their emergence in a co-evolutionary learning environment. We introduce the concept of confidence for players in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results are presented to demonstrate that co-evolutionary learning with coalitions and player confidences can produce IPD game-playing strategies that generalize well.