| 多Agent自动协商的公平性研究 |
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| 引用本文： | 郑素娥,左保河,孙甜.多Agent自动协商的公平性研究[J].计算机工程与应用,2011,47(1):238-241. |
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| 作者姓名： | 郑素娥 左保河 孙甜 |
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| 作者单位： | 1.华南理工大学 计算机科学与工程学院，广州 510006 2.华南理工大学 软件学院，广州 510006 |
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| 摘 要： | 为了能够快速、高效地进行Agent协商，构建一个优化的多Agent协商模型。在这个模型的基础上，提出了一个基于协商各方公平性的协商学习算法。算法采用基于满意度的思想评估协商对手的提议，根据对方Agent协商历史及本次协商交互信息，通过在线学习机制预测对方Agent协商策略，动态得出协商妥协度并向对方提出还价提议。最后，通过买卖协商仿真实验验证了该算法的收敛性，表明基于该算法的模型工作的高效性、公平性。
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| 关 键 词： | Agent 效用 满意度 协商历史 在线学习 |
| 收稿时间： | 2009-4-24 |
| 修稿时间： | 2009-7-16
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Research on multi-issue Agent negotiation based on fairness |
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| ZHENG Sue,ZUO Baohe,SUN Tian.Research on multi-issue Agent negotiation based on fairness[J].Computer Engineering and Applications,2011,47(1):238-241. |
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| Authors: | ZHENG Sue ZUO Baohe SUN Tian |
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| Affiliation: | 1.School of Computer Science & Engineering，South China University of Technology，Guangzhou 510006，China 2.School of Software Engineering，South China University of Technology，Guangzhou 510006，China |
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| Abstract: | An optimized multi-Agent negotiation model is built in order to make the Agent negotiations quick.Based on this model,a negotiation learning algorithm that considers the fairness of both negotiators is introduced.This algorithm evaluates offers from the opponent Agent based on the satisfaction degree,learning online to get the opponent's knowledge from interactive instances of history and negotiation of this time,making concessions dynamically based on fair object.Through building the trading negotiation si... |
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| Keywords: | Agent utility satisfaction degree negotiation history learning online
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