中文摘要：

Agent之间的多问题协商（multi-issue negotiation）是一个复杂的动态交互过程．解决协商之前的对象选择问题在电子商务中有着重要的应用价值．为了提高多问题协商的准确性和购物Agent的效用，主要解决协商前的销售Agent的选择问题．为了充分利用协商历史，实现探索（exploration）和利用（exploitation）的折衷，把销售Agent的选择问题转变成K臂赌博机问题（K-armed bandit problem）来求解．提出了信任和声誉的度量模型，结合K臂赌博机问题的求解技术，采用学习机制，提出了几个确定奖励分布的改进算法．最后，以模拟协商过程为基础，将改进算法、信任和声誉有机地结合起来，提高了选择销售Agent的准确性和实用性．几个实验都说明了该工作在应用中的有效性．

英文摘要：

Multi-Issue negotiation between Agents is a complicated course in which negotiating Agents mutually exchange offers. Solving the problem of choosing seller before negotiation has important practical value in e-commerce. The problem is solved in this paper to improve accuracy of the multi-issue negotiation and buying Agent＇s utility. In order to fully utilize negotiation history, tradeoff exploration and exploitation, the problem of choosing seller is transformed into a K-armed bandit problem. A model for measuring trust and reputation is presented, several improved algorithms, which are used to learn reward distribution and combine learning with technologies for K-armed bandit problem, are presented. Finally, the combination of the improved algorithms, the trust and reputation improves the accuracy and practicability of choosing a selling Agent. Several experiments prove validity of the work in application.