Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation

  • Niels van Galen LastEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 383)


In many situations, a form of negotiation can be used to resolve a problem between multiple parties. However, one of the biggest problems is not knowing the intentions and true interests of the opponent. Such a user profile can be learned or estimated using biddings as evidence that reveal some of the underlying interests. In this paper we present a model for online learning of an opponent model in a closed bilateral negotiation session. We studied the obtained utility during several negotiation sessions. Results show a significant improvement in utility when the agent negotiates against a state-of-the-art Bayesian agent, but also that results are very domain-dependent.


Multiagent System Autonomous Agent Average Utility Bilateral Negotiation Opposing Party 
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.
    Baarslag, T., Hindriks, K., Jonker, C., Kraus, S., Lin, R.: The First Automated Negotiating Agents Competition (ANAC 2010). In: Ito, T., et al. (eds.) New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383, pp. 113–135. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Coehoorn, R., Jennings, N.: Learning on opponent’s preferences to make effective multi-issue negotiation trade-offs. In: … of the 6th International Conference on … (January 2004)Google Scholar
  3. 3.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24(3-4), 159–182 (1998)CrossRefGoogle Scholar
  4. 4.
    Fatima, S., Wooldridge, M., Jennings, N.: Optimal negotiation strategies for agents with incomplete information. In: Intelligent Agents VIII (January 2002)Google Scholar
  5. 5.
    Hindriks, K., Jonker, C., Kraus, S., Lin, R.: Genius: negotiation environment for heterogeneous agents. In: Proceedings of The 8th … (January 2009)Google Scholar
  6. 6.
    Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 331–338 (2008)Google Scholar
  7. 7.
    Mudgal, C., Vassileva, J.: Bilateral negotiation with incomplete and uncertain information: A decision-theoretic approach using a model of the opponent. In: Cooperative Information Agents IV-The Future of Information Agents in Cyberspace, pp. 1–43 (2004)Google Scholar
  8. 8.
    Rosenschein, J., Genesereth, M., University, S.: Deals among rational agents. Citeseer (January 1984)Google Scholar
  9. 9.
    Rosenschein, J.S., Zlotkin, G.: Rules of encounter: designing conventions for automated negotiation among computers. MIT Press, Cambridge (1994)Google Scholar
  10. 10.
    Soo, V.W., Hung, C.A.: On-line incremental learning in bilateral multi-issue negotiation. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: part 1, p. 315 (2002)Google Scholar
  11. 11.
    Zeng, D., Sycara, K.: How can an agent learn to negotiate? Intelligent Agents III Agent Theories (January 1997)Google Scholar
  12. 12.
    Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human-Computers Studies (January 1998)Google Scholar
  13. 13.
    Zlotkin, G., Rosenschein, J.: Negotiation and task sharing among autonomous agents in cooperative domains. In: Proceedings of the Eleventh International … (January 1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Man-Machine Interaction, Faculty of EEMCSDelft University of TechnologyDelftThe Netherlands

Personalised recommendations