Abstract
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.
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Yang, SR., Cho, SB. (2003). Evolutionary Learning of Multiagents Using Strategic Coalition in the IPD Game. In: Lee, J., Barley, M. (eds) Intelligent Agents and Multi-Agent Systems. PRIMA 2003. Lecture Notes in Computer Science(), vol 2891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39896-7_5
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DOI: https://doi.org/10.1007/978-3-540-39896-7_5
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