A Fuzzy Negotiation Model with Genetic Algorithms

  • Dongsheng Zhai
  • Yuying Wu
  • Jinxuan Lu
  • Feng Yan
Conference paper
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT, volume 251)


An offer in a fuzzy negotiation model is rejected or accepted by acceptability based on fuzzy set theory and membership functions. Since different issues have different effect on negotiators, the combined concession in the multi-issue negotiation for negotiators and negotiation agents and genetic learning mechanism are adopted to update their beliefs about incomplete information. The fuzzy negotiation model with genetic algorithms is more practical than the traditional negotiation model.


Genetic Algorithm Membership Function Multiple Criterion Decision Making Negotiation Protocol Negotiation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Samuel P.M. Choi, Jiming Liu, Sheung-Ping Chan. “A Genetic Agent-based Negotiation System”. Computer Networks 37, 2001, 195–204.CrossRefGoogle Scholar
  2. 2.
    Ren-Jye Dzeng, Yu-Chun Lin. “Intelligent Agents for Supporting Construction Procurement Negotiation”. Expert Systems with Applications 27, 2004, 107–119CrossRefGoogle Scholar
  3. 3.
    Raymond Y.K. Lau, Towards Genetically Optimised Multi-Agent Multi-Issue Negotiations, Proceedings of the 38th Hawaii International Conference on System Sciences, 2005, 1–10Google Scholar
  4. 4.
    F. Bergenti, A. Poggi, An Agent-based Approach to Manage Negotiation Protocols in Rexible CSCW Systems, Proceeding of.4th International Conference on Autonomous Agents, Barcelona Spain, 2000, 267–268.Google Scholar
  5. 5.
    Raymond Y.K. Lau, Maolin Tang, On Wong, Towards Genetically Optimised Responsive Negotiation Agents, Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’04), 2004, 1–7Google Scholar
  6. 6.
    W. C. Stirling. Social Utility Functions-Part 1: Theory, IEEE Transaction Systems, Man, Cybernetics. C, Applications and Reviews, vol. 35, no. 4, 2005 522–532Google Scholar
  7. 7.
    W. C. Stirling, R.L. Frost. Social Utility Functions-Part 2: Theory, IEEE Transaction Systems, Man, Cybernetics. C, Applications and Reviews, vol. 35, no. 4, 2005 533–543Google Scholar
  8. 8.
    Y.Y Wu, J.X Lu, “A Fuzzy Negotiation Model of E-commerce”, Journal of System Science and Information, 2006, Vol.4, No. 1, 33–37Google Scholar
  9. 9.
    S.L Zhao, G.R Jiang, T.Y Huang. An Analysis Model to Agent Ability Report Deception in Multi-agent Cooperation, Proceedings of 2005 International Conference on Management Science & Engineering, 2005Google Scholar
  10. 10.
    S.L Zhao, G.R Jiang, T.Y Huang. The Deception Detection and Restraint in Multi-agent System, Proceedings of 17th IEEE International Conference on Tools with Artificial Intelligence, 2005Google Scholar
  11. 11.
    D.S Zhai, J.X Lu, Y.Y Wu, “A Program of Automatic Negotiation in E-commerce”, Journal of Information and Decision Science, 2006, Vol. 1, No. 1, 7–11Google Scholar
  12. 12.
    Sheng-Hshiung Tsaur, T.Y Chang and C.H Yen, “The Evaluation of Airline Service Quality by Fuzzy MCDM”, Tourism Management, 2002 (23), pp. 107–115.CrossRefGoogle Scholar
  13. 13.
    M. Bo, F. Wei, A Negotiation Model Based on Fuzzy Multiple Criteria Decision Making Method, Proceedings of the Fourth International Conference on Computer and Information Technology, CIT’04, 2004, 1039–1044Google Scholar
  14. 14.
    C.-B. Cheng, C.-C. H. Chan, and K.-C. Lin, Intelligent agents for e-Marketplace: Negotiation with Issue Trade-offs by Fuzzy Inference Systems, Decision Support System.Google Scholar
  15. 15.
    Y.X Meng, B. Meng. An Agent-based Negotiation Support System with Fuzzy Multi-objective Decision-making Method, Proceedings of ICSSSM’ 05. 2005 International Conference on Services Systems and Services Management, 2005, 1141–1144 Vol. 2CrossRefGoogle Scholar
  16. 16.
    Lai. K.R. Chung Hsien Lan. Development of an Assessment Agent to Promote the Learning Effectiveness in a Computer Supported Collaborative Learning Environment, Proceeding of Fifth IEEE International Conference on Advanced Learning Technologies, 2005. ICALT 2005. 354–358Google Scholar
  17. 17.
    Bonnie Rubenstein-Montano, Ross A. Malaga, A Weighted Sum Genetic Algorithm to Support Multiple-Party Multiple-Objective Negotiations, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 4, 2002, 366–377CrossRefGoogle Scholar
  18. 18.
    Ravindra Krovi, Arthur C. Graesser, William E. Pracht, Agent Behaviors in Virtual Negotiation Environments, IEEE Transactions on Systems, “Man, and Cybernetics-Part C”: Applications and Reviews, Vol. 29, No. 1, 1999, 15–25Google Scholar
  19. 19.
    Y.Y Wu, J. Lu, F. Yan, A Fuzzy Negotiation Model of e-Commerce and Its Implementation, Technology Management for the Global Future, PICMET 2006 Proceedings, Vol.3, 9–13 July, Istanbul, Turkey, 1180–1185Google Scholar
  20. 20.
    Z. Ren*, C.J. Anumba, O.O. Ugwu. The Development of a Multi-agent System for Construction Claims Negotiation. Advances in Engineering Software 34, 2003, 683–696.CrossRefGoogle Scholar
  21. 21.
    Wei-Po Lee. Towards Agent-based Decision Making in the Electronic Marketplace: Interactive Recommendation and Automated Negotiation. Expert Systems with Applications 27, 2004.Google Scholar
  22. 22.
    Chun, A., Wai, H., & Wong, R.. Optimizing Agent-based Meeting Scheduling through Preference Estimation. Engineering Applications of Artificial Intelligence, 16, 727–743Google Scholar
  23. 23.
    Chang-Shing Lee, Chen-Yu Pan. An Intelligent Fuzzy Agent for Meeting Scheduling Decision Support System. Fuzzy Sets and Systems 142, 2004, 467–488MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Dongsheng Zhai
    • 1
  • Yuying Wu
    • 1
  • Jinxuan Lu
    • 1
  • Feng Yan
    • 1
  1. 1.School of Economics and ManagementBeijing University of TechnologyBeijingChina

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