Co-evolutionary learning with strategic coalition for multiagents

In a dynamic system, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to changing 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 the dynamic system, and propose strategic coalition to obtain superior adaptive agents and simulate its emergence in a co-evolutionary learning environment. Also, we introduce the concept of confidence for agents in a coalition and show how such confidence helps improving the generalization ability of the evolved agents using strategic coalition. Experimental results show that co-evolutionary learning with coalition and confidence can produce better performing agents that generalize well against unseen agents.