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A Machine Learning Approach to Automated Negotiation

Zhang Huaxiang(张化祥) Zhang Liang Huang Shangteng Ma Fanyuan

高技术通讯（英文版）2004，Vol.10Issue(3)：61-65,5.

A Machine Learning Approach to Automated Negotiation

Zhang Huaxiang(张化祥) ¹Zhang Liang ²Huang Shangteng ²Ma Fanyuan²

作者信息

1. Information and Management School, Shandong Normal University, Jinan 250014, P.R.China
2. Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, P.R.China
折叠

摘要

Abstract

Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents' average Q values in each negotiation stage. The delayed reward is used to generate agents' offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.

关键词

agent/negotiation/machine learning

Key words

agent/negotiation/machine learning

分类

信息技术与安全科学

引用本文复制引用

Zhang Huaxiang(张化祥),Zhang Liang,Huang Shangteng,Ma Fanyuan..A Machine Learning Approach to Automated Negotiation[J].高技术通讯（英文版）,2004,10(3):61-65,5.

基金项目

Supported by the High Technology Research and Development Programme of China. （）

高技术通讯（英文版）

OAEI

ISSN：1006-6748

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