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Negotiation decision model based on transductive
support vector machine |
| AI Jie-qing1,2, GAO Ji1, PENG Yan-bin3, ZHENG Zhi-jun3 |
1.Institute of Artificial Intelligence,Zhejiang University,Hangzhou 310027, China; 2. Information Center, Guangdong Power
Grid Corporation, Guangzhou, 510000; 3. Zhejinang University of Science and Technology, Hangzhou 310023, China |
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Abstract The confidentiality of information in e-commerce activities leads to negotiation participants are unable to get the opponent’s utility function, thereby affecting the negotiation performance. To solve this, a bilateral and multi-issue negotiation model based on transductive support vector machine (TSVM-NM) was proposed. In this model, the proposals generated in the procedure of negotiation are stored in negotiation history database. The model constructs labeled data and unlabeled data by making full use of the implicit information in negotiation history and analyzing that whether those proposals fall in opponent’s acceptable utility zone. Those data become the training samples of TSVM. Then the estimation of opponent’s utility function was obtained by learning the training samples. With the combination of self’s utility function and the estimation of opponent’s utility function, a constrained optimization problem is formed, which is to be resolved by particle swarm optimization (PSO). The optimal solution is the self’s counter-offer. Experimental results show that this model can shorten the negotiation time and increase both the success rate of negotiation and the joint utility, in the environments where information is private and the prior knowledge is not available.
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Published: 24 July 2012
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基于直推式支持向量机的协商决策模型
为了解决在电子商务活动中由于信息的保密性协商参与者无法获得对手效用函数,进而影响双方协商性能的问题,提出一种基于直推式支持向量机(TSVM)算法的双边多议题协商决策模型.该模型利用协商历史中隐含的信息,分析协商过程中产生的建议是否落在对手效用可接受区间内,构造有标记和无标记的训练样本,并通过直推式支持向量机来学习这些训练样本,得到协商对手效用函数的估计,然后与己方效用函数相结合构成一个约束优化问题,利用粒子群算法求解此优化问题得到己方的最优反建议.实验结果表明：此模型在信息保密和缺乏先验知识的情况下,能够兼顾对手效用做出协商决策,增加了双方的协商成功率和联合效用值,并能够有效减少协商时间.
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