| A Machine Learning Approach to Automated Negotiation |
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| 作者姓名： | 张化祥 Zhang Liang Huang Shangteng Ma Fanyuan |
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| 作者单位： | [1]DepartmentofComputerScienceandEngineering,ShanghaiJiaotongUniversity,Shanghai200030,P.R.China [2]InformationandManagementSchool,ShandongNormalUniversity,Jinan250014,P.R.China |
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| 基金项目： | SupportedbytheHighTechnologyResearchandDevelopmentProgrammeofChina . |
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| 摘 要： | 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.
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| 关 键 词： | 机器学习 自动对话 人工智能技术 对话模型 |
A Machine Learning Approach to Automated Negotiation |
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| Zhang Huaxiang,Zhang Liang,Huang Shangteng,Ma Fanyuan.A Machine Learning Approach to Automated Negotiation[J].High Technology Letters,2004,10(3):61-65. |
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| Authors: | Zhang Huaxiang Zhang Liang Huang Shangteng Ma Fanyuan |
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| 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. |
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| Keywords: | agent negotiation machine learning |
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