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The automatization of electronic commerce negotiation has become the focus of more and more attention. As one of the core of automated negotiation, strategy is the method employed by agents to maximize their own benefits. The design of negotiation strategy is affected by lots of factors, such as negotiation deadline, the type of resources, the characteristics of opponents, the numbers of competitors, etc. Though lots of work have been done on negotiation strategy, there still lack a uniform strategy framework. In this paper, a general description framework for multiagent negotiation is proposed. Then solutions to the decision models are given and the existence of the Nash equilibrium in agents' attitude selection at the beginning of negotiations is analyzed. To enable agents to adapt their attitudes according to negotiation duration, the supply and demand ratio, and the characteristics of opponent, we introduce an adaptation function. This function is constructed on the basis of a history-based learning algorithm. Furthermore, the attitude-adaptation strategy and the fixed-attitude strategies are compared by simulation.

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