北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2012, Vol. 35 ›› Issue (5): 22-25.doi: 10.13190/jbupt.201205.22.pengyb

• 论文 • 上一篇    下一篇

对手偏好主动学习驱动的协商框架

彭艳斌, 艾解清, 李吉明   

  1. 1. 浙江科技学院 信息与电子工程学院2. 浙江大学 计算机学院3. 浙江警察学院 刑事科学技术系
  • 收稿日期:2011-09-07 修回日期:2012-06-10 出版日期:2012-10-28 发布日期:2012-07-06
  • 通讯作者: 彭艳斌 E-mail:pyb2010@126.com
  • 作者简介:彭艳斌(1979-),男,副教授,博士,E-mail:pyb2010@126.com
  • 基金资助:

    国家自然科学基金项目(61175058);浙江省自然科学基金项目(Y1100036);浙江省教育厅科研计划基金项目(Y201016929,Y201222997)

Negotiation Framework Driven by Active Learning of Opponents Preference

PENG Yan-bin, AI Jie-qing, LI Ji-ming   

  1. 1. School of Information and Electronic Engineering, Zhejiang University of Science and Technology2. College of Computer Science and Technology, Zhejiang University3. Department of Forensic Science, Zhejiang Police College
  • Received:2011-09-07 Revised:2012-06-10 Online:2012-10-28 Published:2012-07-06
  • Contact: Yan-Bin PENG E-mail:pyb2010@126.com

摘要:

针对自动化协商问题,提出一种基于主动学习算法的对手协商偏好学习方法. 在该方法中,协商过程表示为建议序列,将建议序列映射到出价轨迹特征空间,建立训练样本集. 在激烈竞争的电子商务环境中,样本标记的成本较高,引入主动学习算法后,在预算范围内,提高了对手协商偏好预测的精度. 实验数据表明,该方法能在少量有标记训练样本下获得良好的预测能力,减少了协商回合数,提高了协商总效用.

关键词: 电子商务, 协商框架, 主动学习, 协商偏好

Abstract:

Aiming at solving automated negotiation problem, an active learning based method was proposed to learn opponents negotiation preference. The process of negotiation was viewed as a proposals sequence which can be mapped into bidding trajectory feature space to form sample set. Due to fierce competition, the cost of labeling samples is high. Therefore, active learning algorithm was applied to improve the prediction accuracy of opponents negotiation preference within budget. The experimental results show that the proposed method has better prediction ability, which can reduce the number of negotiation steps and increase the overall utility of negotiation.

Key words: electronic commerce, negotiation framework, active learning, negotiation preference

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