北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2009, Vol. 32 ›› Issue (3): 1-4.doi: 10.13190/jbupt.200903.1.lij

• 论文 •    下一篇

学习机制在电子商务中的应用

李 剑, 牛少彰   

  1. 北京邮电大学 灾备技术国家工程实验室
  • 收稿日期:2008-06-26 修回日期:2009-01-06 出版日期:2009-06-28 发布日期:2009-06-28
  • 通讯作者: 李剑

Learning Mechanism and Its Application in E-commerce

LI Jian, NIU Shao-zhang   

  1. National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications
  • Received:2008-06-26 Revised:2009-01-06 Online:2009-06-28 Published:2009-06-28
  • Contact: LI Jian

摘要:

为了削除基于智能体电子商务双边多议题协商当中智能体之间的不完全信息,加速协商的过程,提高协商的效率,提出一种分类学习机制来学习协商中竞争对手的不完全信息,特别是协商中议题的权重信息.该方法主要是通过协商过程中将不同的让步议题分配给不同类别权重的方法,从而学习到对方协商议题的权重信息.在实验中,分别对没有学习的智能体和带有分类学习的智能体的协商结果进行对比,结果表明在求解双边多议题协商问题的时候,带有学习机制的智能体可以使得协商中的智能体高效达到协商的满意解. 这个结果说明,在基于智能体的电子商务当中带有学习的智能体可以提高电子商务中协商的效率.

关键词: 电子商务, 双边多议题协商, 智能体, 学习机制

Abstract:

To decrease the incomplete information between agents in MAS(multi-agent system)based e-commerce, accelerate the process of negotiation, and enhance the efficiency of negotiation, a classified learning mechanism is provided in bilateral multi-issue negotiation to learn some incomplete information especially the incomplete information about agent opponent’s negotiation issue weight. The opponent’s issue weight is learned by assigning different concession issue to different classified weight. In the experiment, two kinds of negotiation agents are used to compare, one is the agent with no learning, and the other is the agent with classified learning mechanism. The experimental results show that the agent with learning mechanism can negotiate more efficiently than that of no learning agent. The agent with classified learning mechanism can enhance the efficiency of negotiation in agent based ecommerce.

Key words: e-commerce, bilateral multi-issue negotiation, agent, learning mechanism