Please wait a minute...
浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (6): 967-973    DOI: 10.3785/j.issn.1008-973X.2012.06.002
计算机技术     
基于直推式支持向量机的协商决策模型
艾解清1,2, 高济1, 彭艳斌3, 郑志军3
1. 浙江大学 人工智能研究所,浙江 杭州 310027;2. 广东电网公司信息中心,广东 广州 510000;
3. 浙江科技学院,浙江 杭州 310023
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
 全文: PDF  HTML
摘要：

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.
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.
出版日期: 2012-07-24
:  TP 181  
基金资助:

国家“863”高技术研究发展计划资助项目(2007AA01Z187); 国家自然科学基金资助项目(60775029); 浙江省自然科学基金资助项目(Y1100036);浙江省教育厅科研计划资助项目(Y201016929).
通讯作者: 高济, 教授,博导.     E-mail: dotrai@126.com
作者简介: 艾解清(1982—),男,博士生,从事智能计算、机器学习等研究. E-mail: dotrai@126.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　

引用本文:

艾解清, 高济, 彭艳斌, 郑志军. 基于直推式支持向量机的协商决策模型[J]. J4, 2012, 46(6): 967-973.

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.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2012.06.002    或    http://www.zjujournals.com/eng/CN/Y2012/V46/I6/967

［1］ JENNINGS N R, FARATIN P, LOMUSCIO A R. Automated negotiation: prospects, methods and challenges ［J］. International Journal of Group Decision and Negotiation, 2001, 10(2): 199-215.
［2］ ZENG D J, SYCARA K. Bayesian learning in negotiation ［J］. International Journal of HumanComputer Studies, 1998, 48(1): 125-141.
［3］ COEHOORN R M, JENNINGS N R. Learning an opponent’s preferences to make effective multiissue negotiation tradeoffs ［C］ ∥ Proceedings of the 6th International Conference on ECommerce. New York: ACM, 2004: 59-68.
［4］ 王黎明,黄厚宽,柴玉梅.基于信任和 K 臂赌博机问题选择多问题协商对象［J］. 软件学报, 2006, 17(12): 2537-2546.
WANG Liming, HUANG Houkuang, CHAI Yumei. Choosing multiissue negotiating object based on trust and Karmed bandit problem ［J］. Journal of Software, 2006, 17(12): 2537-2546.

［5］ 高坚,张伟.多Agent系统中双边多指标自动协商的ACEA算法 ［J］. 计算机研究与发展, 2006, 43(6): 1104-1108.
GAO Jian, ZHANG Wei. An accelerating chaos evolution algorithm of bilateral multiissue automated negotiation in MAS ［J］. Journal of Computer Research and Development, 2006, 43(6): 1104-1108.
［6］ 程昱,高济,古华茂.基于机器学习的自动协商决策模型 ［J］. 软件学报, 2009, 20(8): 2160-2169.
CHENG Yu, GAO Ji, GU Huamao. Automated negotiation decision model based on machine learning ［J］. Journal of Software, 2009, 20(8): 2160-2169.
［7］ GAMMERMAN A, VAPNIK V, VOWK V. Learning by transduction ［C］∥ Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Wisconsin: IEEE, 1998: 148-156.
［8］ JOACHIMS T. Transductive inference for text classification using support vector machines ［C］ ∥ Proceedings of the 16th International Conference on Machine Learning (ICML). San Francisco: Morgan Kaufmann, 1999: 200-209.
［9］ 陈毅松,汪国平,董士海.基于支持向量机的渐近直推式分类学习算法 ［J］.软件学报, 2003, 14(3): 451-460.
CHEN Songyi, WANG Guoping, DONG Shihai. A progressive transductive inference algorithm based on support vector machine ［J］. Journal of Software, 2003, 14(3): 451-460.
［10］ KENNEDY J, EBERTHART R. Particle swarm optimization ［C］ ∥ Proceedings IEEE International Conference on Neural Networks. Piscataway: IEEE, 1995: 1942-1948.
［11］ 倪庆剑,张志政,王蓁蓁.一种基于可变多簇结构的动态概率粒子群优化算法 ［J］. 软件学报, 2009, 20(2): 339-349.
NI Qingjian, ZHANG Zhizheng, WANG Zhenzhen. Dynamic probabilistic particle swarm optimization based on varying multicluster structure ［J］. Journal of Software, 2009, 20(2): 339-349.
［12］ FARATIN P, SIERRA C, JENNINGS N R. Using similarity criteria to make issue tradeoffs in automated negotiations ［J］. Artificial Intelligence, 2002, 142(2): 205-237.
[1] 林亦宁, 韦巍, 戴渊明. 半监督Hough Forest跟踪算法[J]. J4, 2013, 47(6): 977-983.
[2] 李侃,黄文雄,黄忠华. 基于支持向量机的多传感器探测目标分类方法[J]. J4, 2013, 47(1): 15-22.
[3] 王洪波, 赵光宙, 齐冬莲, 卢达. 一类支持向量机的快速增量学习方法[J]. J4, 2012, 46(7): 1327-1332.
[4] 潘俊, 孔繁胜, 王瑞琴. 局部敏感判别直推学习机[J]. J4, 2012, 46(6): 987-994.
[5] 金卓军, 钱徽, 朱淼良. 基于倾向性分析的轨迹评测技术[J]. J4, 2011, 45(10): 1732-1737.
[6] 顾弘, 赵光宙. 广义局部图像距离函数下的图像分类与识别[J]. J4, 2011, 45(4): 596-601.
[7] 罗建宏,陈德钊. 兼顾正确率和差异性的自适应集成算法及应用[J]. J4, 2011, 45(3): 557-562.
[8] 商秀芹, 卢建刚, 孙优贤. 基于遗传规划的铁矿烧结终点2级预测模型[J]. J4, 2010, 44(7): 1266-1269.
[9] 徐磊, 赵光宙, 顾弘. 成对耦合分类器的多球体预处理方法[J]. J4, 2010, 44(2): 237-242.