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J4  2012, Vol. 46 Issue (6): 967-973    DOI: 10.3785/j.issn.1008-973X.2012.06.002
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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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.

Published: 24 July 2012
CLC:  TP 181  
Cite this article:

AI Jie-qing, GAO Ji, PENG Yan-bin, ZHENG Zhi-jun. Negotiation decision model based on transductive
support vector machine. J4, 2012, 46(6): 967-973.

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