基于学习的多Agent多议题协商优化研究

以买方Agent的观点，对从交易平台上获得的卖方Agent的历史协商信息进行分析，并根据其特点做初步过滤。在此基础上，针对现有协商模型中存在的问题，提出了一个Agent协商对手选择算法和相应的交互机制，并验证了其可行性。该算法可用于Agent协商开始前协商对手的选择和初始信念的更新，对Agent在协商中策略的选择和执行具有指导作用，能有效提高Agent在协商中的效用及效率。

Integrated-utility based on study in multi-Agent multi-issues negotiation

This paper states the algorithm from the respective of the buyer Agent.The negotiation history of the opponent Agent is analyzed firstly,which is obtained from the trade platform.Then it is pre-filtered on the characteristic of data structures.On the basis of the above,it proposes a negotiation rival choosing algorithm and alternation mechanism after analyzing the problems of the present negotiation models.This algorithm can be used to choose negotiation rival and update the initial beliefs of the Agent before negotiation,and plays a guidable role in selecting and implementing the tactics in Agent negotiation,and improve the utility and efficiency in the negotiation.