Coalitions among computationally bounded agents
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Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Guest Editors' Introduction: Knowledge Systems for Coalition Operations
IEEE Intelligent Systems
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IEEE Intelligent Systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Speciation as automatic categorical modularization
IEEE Transactions on Evolutionary Computation
An Evolutionary Solution for Cooperative and Competitive Mobile Agents
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
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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.