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STUDY ON DYNAMIC BARGAINING SYSTEM BASED ON MACHINE LEARNING

【摘要】:正In this paper, aiming at the learning deficiency in the given bargaining systems, the decimal code, instead of binary code, is adopted to prevent variables from going beyond limitative scope, which will cause exceptional strategy. Furthermore, a dynamic bargaining system is presented based on machine learning (MLDBS). The result of experiment shows that the agent in MLDBS not can only identify its opponents successfully but can change its strategy in term of different opponents in a bargaining process. If is shown by the experimental datum that MLDBS increase successful times of bargaining and enhance the average payoff of agent.

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