Exploiting coalition in co-evolutionary learning
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.
Authors: Yeon-Gyu Seo, Sung-Bae Cho, Xin Yao
Published in: IEEE Congress on Evolutionary Computation (CEC) (2002)