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    王黎明, 黄厚宽. 一个基于多阶段的多Agent多问题协商框架[J]. 计算机研究与发展, 2005, 42(11): 1849-1855.
    引用本文: 王黎明, 黄厚宽. 一个基于多阶段的多Agent多问题协商框架[J]. 计算机研究与发展, 2005, 42(11): 1849-1855.
    Wang Liming, Huang Houkuan. A Multistage-Based Framework for Multi-Agent Multi-Issue Negotiation[J]. Journal of Computer Research and Development, 2005, 42(11): 1849-1855.
    Citation: Wang Liming, Huang Houkuan. A Multistage-Based Framework for Multi-Agent Multi-Issue Negotiation[J]. Journal of Computer Research and Development, 2005, 42(11): 1849-1855.

    一个基于多阶段的多Agent多问题协商框架

    A Multistage-Based Framework for Multi-Agent Multi-Issue Negotiation

    • 摘要: 多问题协商是电子交易中的关键问题.多Agent技术的不断成熟为这个问题的解决提供了有效的途径.提出了一个以理性Agent为基础的基于多阶段的多问题协商框架,该框架在时间约束下适用于信息不完全的场景,它描述了多问题的价格协商.为了降低多问题协商的复杂性,它将多问题协商分解为多阶段协商,每个阶段的大小(问题数)相同.阶段数和顺序在协商前确定,每个阶段中的问题顺序在协商中确定.在阶段大小相同的情况下,对给定协商问题的分解,框架能给出优化协商议程(agenda).尤其是框架能为参与协商的Agent建立学习系统(LS),以增强Agent的学习能力.最后基于这个框架实现了一个原型系统,原型系统证明这个框架是有效的.

       

      Abstract: Multi-issue negotiation is a key problem in electronic bargaining. Multi-agent technology provides effective solution for the problem. Presented in this paper is a multistage-based framework for multi-issue negotiation under time constraint in an incomplete information setting based on rational agents. The framework describes negotiation over the price of multi-issue. In this framework, in order to reduce complexity of the multi-issue negotiation, multi-issue negotiation is decomposed into multistage negotiation, and each stage has the same size (issues number). The order and number of stages are decided exogenously, and the order of issues in each stage is decided endogenously. The framework can give optimal negotiation agenda for a given decomposition of the bargaining problem based on the same size of stages. In particular, the framework can also strengthen the learning capability of participating agents by building learning system (LS) for them. Finally, a prototype system based on the negotiation framework is implemented, and the prototype system proves that the framework is effective.

       

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