| 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. |
| 关键词: Agent 协商 K臂赌博机问题 信任 声誉 效用 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60443003 (国家自然科学基金) |
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| Choosing Multi-Issue Negotiating Object Based on Trust and K-Armed Bandit Problem |
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WANG Li-Ming,HUANG Hou-Kuan,CHAI Yu-Mei
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| Abstract: |
| 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. |
| Key words: Agent negotiation K-armed bandit problem trust reputation utility |
