文件名称:AutomatedNegotiatioDecisionModelasedonMachineLearn
- 所属分类：
- 人工智能/神经网络/遗传算法
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- [PDF]
- 上传时间：
- 2012-11-26
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- 502kb
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模型利用协商历史中隐含的信息自动对数据进行标注以形成训练样本，用最小二乘支持向量回
归机学习此样本得到对手效用函数的估计，然后结合自己和对手的效用函数构成一个约束优化问题，用遗传算法求
解此优化问题，得到的最优解就是己方的反建议．实验结果表明，在信息保密和没有先验知识的条件下，此模型仍然
表现出较高的效率和效用-The proposed model labels the negotiation history data automatically by making full use of the implicit
information in negotiation history．Then，the labeled data become the training samples of least-squares support
vector machine that outputs the estimation of opponent’s utility function．After that，the self s utility function and
the estimation of opponent’s utility function constitute a constraint optimization problem that will be further figured
out by genetic algorithm．The optimal solution is the counter-ofer of onesel~ Experimental results show that the
proposed model is efective and efi cient in environments where information is private and the prior knowledge is
not available．
归机学习此样本得到对手效用函数的估计，然后结合自己和对手的效用函数构成一个约束优化问题，用遗传算法求
解此优化问题，得到的最优解就是己方的反建议．实验结果表明，在信息保密和没有先验知识的条件下，此模型仍然
表现出较高的效率和效用-The proposed model labels the negotiation history data automatically by making full use of the implicit
information in negotiation history．Then，the labeled data become the training samples of least-squares support
vector machine that outputs the estimation of opponent’s utility function．After that，the self s utility function and
the estimation of opponent’s utility function constitute a constraint optimization problem that will be further figured
out by genetic algorithm．The optimal solution is the counter-ofer of onesel~ Experimental results show that the
proposed model is efective and efi cient in environments where information is private and the prior knowledge is
not available．
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AutomatedNegotiatioDecisionModelasedonMachineLearning.PDF