Evolutionary Learning of Multiagents Using Strategic Coalition in the IPD Game

Social and economic systems consist of complex interactions among its members. Their behaviors become adaptive according to changing environment. In many cases, an individual’s behaviors 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 a simple model to deal with complex problems for dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results show that co-evolutionary learning with coalitions and confidence can produce better performing strategies that generalize well in dynamic environments.