Towards an Experience Based Negotiation Agent

  • Wai Yat Wong
  • Dong Mei Zhang
  • Mustapha Kara-Ali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1860)


Current E-Commerce trading agents with electronic negotiation facilities usually use predefined and non-adaptive negotiation mechanisms [5]. This paper presents a negotiation agent that applies Case-Based Reasoning techniques to capture and re-use previously successful negotiation experiences. This Experience Based Negotiation (EBN) agent provides adaptive negotiation strategies that can be generated dynamically and are context-sensitive. We demonstrate this negotiation agent in the context of used-car trading. This paper describes the negotiation process and the conceptual framework of the EBN agent. It discusses our web based used-car trading prototype, the representation of the used-car trading negotiation experience, and the stages of experience based negotiation. The paper also discusses some experimental observation and illustrates an example of adaptive behaviour exhibited by the EBN agent. We believe that this Experience Based Negotiation framework can enhance the negotiation skills and performance of current trading agents.


Negotiation Process Negotiation Strategy Automate Negotiation Seller Agent Trading Agent 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bertsekas, D.P.: Dynamic Programming and Optimal Control. Athena Scientific, Belmont (1995)zbMATHGoogle Scholar
  2. 2.
    Chavez, A., Dreilinger, D., Guttman, R., Maes, P.: A real-life experiment in creating an agent marketplace. In: Proceedings of the Second International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, PAAM 1997 (1997)Google Scholar
  3. 3.
    Cyert, R.M., DeGroot, M.H.: Bayesian Analysis and Uncertainty in Economic Theory. Rowman & Littlefield, New York (1987)Google Scholar
  4. 4.
    Kowalczyk, R., Bui, V.: Towards Intelligent Trading Agents. In: The International Conference on Intelligent Systems and Active DSS in Turku/Abo, Finland (1999)Google Scholar
  5. 5.
    Maes, P., Guttman, R.H., Moukas, A.G.: Agents that buy and sell. Communications of The ACM 42(3), 81–91 (1999)CrossRefGoogle Scholar
  6. 6.
    Sandholm, T.: Automated Negotiation: The best terms for all concerned. Communications of The ACM 42(3), 84–85 (1999)CrossRefGoogle Scholar
  7. 7.
    Siriwan, A., Sadananda, R.: An agent-mediated Negotiation Model in Electronic Commerce. In: The Australian Workshop on AI in Electronic Commerce, The Australian Joint Conference on Artificial Intelligence (1999)Google Scholar
  8. 8.
    Sycara, K.: Utility theory in conflict resolution. Annals of Operations research 12, 65–84 (1988)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Sycara, K.: Machine learning for intelligent support of conflict resolution. Decision Support systems 10, 121–136 (1993)CrossRefGoogle Scholar
  10. 10.
    Zeng, D., Sycara, K.: Bayesian Learning in Negotiation. Int. J. Human- Computer Studies 48, 125–141 (1998)CrossRefGoogle Scholar
  11. 11.
    Zhang, D., Wong, W., Kowalczyk, R.: Reusing Previous Negotiation Experiences in Multi-Agent Negotiation. In: Proceedings of Workshop on Agents in Electronic Commerce (WAEC 1999), Hong Kong, December 14 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Wai Yat Wong
    • 1
  • Dong Mei Zhang
    • 1
  • Mustapha Kara-Ali
    • 1
  1. 1.CSIRO Mathematical and Information SciencesNorth RydeAustralia

Personalised recommendations