【摘要】 所提出的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.更多还原

【Abstract】 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-offer of oneself. Experimental results show that the proposed model is effective and efficient in environments where information is private and the prior knowledge is not available.更多还原