Artificial Agents for Discovering Business Strategies for Network Industries
International Journal of Electronic Commerce
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This paper explores the designing of effective artificial agents. It reports some real experiments with artificial agents working on a realistic problem: exploring suppliers' pricing strategies and consumers' consumption strategies in a deregulated stochastic electric power marketplace. Several domain specific constraint-handling techniques in genetic algorithms, which are the core for agents learning, have been implemented and tested in a unified experimental setting, with promising results. The approach can be generalized to other network industries, such gas, water, financial service and telecommunications.