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作者:

Li, Hao (Li, Hao.) | Pang, Ting (Pang, Ting.) | Wu, Yuying (Wu, Yuying.) | Jiang, Guorui (Jiang, Guorui.)

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EI Scopus

摘要:

In order to overcome the lack of adaptability and learning ability of traditional negotiation, we regard supply chain production-marketing collaborative planning negotiation as the research object, design one five-elements negotiation model, adopt a negotiation strategy based on Q-reinforcement learning, and optimize the negotiation strategy by the RBF neural network and predict the information of opponent for adjusting the concession extent. At last, we give a sample that verifies the negotiation strategy can enhance the ability of the negotiation Agents, reduce the negotiation times, and improve the efficiency of resolving the conflicts of production-marketing collaborative planning, comparing to the un-optimized Q-reinforcement learning. Copyright © 2014 SCITEPRESS.

关键词:

Commerce Intelligent agents Machine learning Marketing Multi agent systems Reinforcement learning Supply chains

作者机构:

  • [ 1 ] [Li, Hao]Economics and Management School, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, China
  • [ 2 ] [Pang, Ting]Economics and Management School, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, China
  • [ 3 ] [Wu, Yuying]Economics and Management School, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, China
  • [ 4 ] [Jiang, Guorui]Economics and Management School, Beijing University of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing, China

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年份: 2014

卷: 2

页码: 209-214

语种: 英文

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