• DocumentCode
    2258787
  • Title

    Research on Multi-Agent Automatic Negotiation Based on Machine Learning

  • Author

    Hua, Jiang ; Jing, Yang

  • Author_Institution
    Sch. of Econ. & Manage., Hebei Univ. of Eng., Handan, China
  • Volume
    1
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    186
  • Lastpage
    191
  • Abstract
    At present, applying machine learning theory to the multi-agent automatic negotiation system has become the latest research focus in e-commerce area. The paper aimed at using Bayesian rules to update the environmental information in negotiation, using the Q-learning algorithm of reinforcement-learning to generate the negotiation proposals, and established a multi-agent automatic negotiation model based on machine learning, and expanded the traditional Q-learning algorithm, and designed the dynamic Q-learning algorithm based on the current belief and the recent exploring surplus. Furthermore, the convergence of the algorithm has been verified by experiments.
  • Keywords
    Bayes methods; learning (artificial intelligence); multi-agent systems; Bayesian rule; dynamic Q-learning algorithm; e-commerce; machine learning theory; multiagent automatic negotiation model; reinforcement learning; Bayesian methods; Environmental economics; Information technology; Learning systems; Machine learning; Machine learning algorithms; Multiagent systems; Proposals; Protocols; Technology management; Machine Learning; Multi-Agent Automatic Negotiation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3497-8
  • Type

    conf

  • DOI
    10.1109/IITA.2008.153
  • Filename
    4739561