Advertisement

Bilateral Single-Issue Negotiation Model Considering Nonlinear Utility and Time Constraint

  • Fenghui Ren
  • Minjie Zhang
  • John Fulcher
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 383)

Abstract

Bilateral single-issue negotiation is studied a lot by researchers as a fundamental research issue in agent negotiation. During a negotiation with time constraint, a negotiation decision function is usually predefined by negotiators to express their expectations on negotiation outcomes in different rounds. By combining the negotiation decision function with negotiators’ utility functions, offers can be generated accurately and efficiently to satisfy negotiators expectations in each round. However, such a negotiation procedure may not work well when negotiators’ utility functions are nonlinear. For example, if negotiators’ utility functions are non-monotonic, negotiators may find several offers that come with the same utility; and if negotiators’ utility functions are discrete, negotiators may not find an offer to satisfy their expected utility exactly. In order to solve such a problem caused by nonlinear utility functions, we propose a novel negotiation approach in this paper. Firstly, a 3D model is introduced to illustrate the relationships among utility functions, time constraints and counter-offers. Then two negotiation mechanisms are proposed to handle two types of nonlinear utility functions respectively, ie. a multiple offers mechanism is introduced to handle non-monotonic utility functions, and an approximating offer mechanism is introduced to handle discrete utility functions. Lastly, a combined negotiation mechanism is proposed to handle nonlinear utility functions in general situations. The experimental results demonstrate the success of the proposed approach. By employing the proposed approach, negotiators with nonlinear utility functions can also perform negotiations efficiently.

Keywords

Utility Function Multiagent System Expected Utility 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brzostowski, J., Kowalczyk, R.: Predicting Partner’s Behaviour in Agent Negotiation. In: Proc. of 5th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2006), pp. 355–361 (2006)Google Scholar
  2. 2.
    Faratin, P., Sierra, C., Jennings, N.: Negotiation Decision Functions for Autonomous Agents. Journal of Robotics and Autonomous Systems 24(3-4), 159–182 (1998)CrossRefGoogle Scholar
  3. 3.
    Fatima, S., Wooldridge, M., Jennings, N.: An Agenda-Based Framework for Multi-Issue Negotiation. Artificial Intelligence 152(1), 1–45 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Fatima, S., Wooldridge, M., Jennings, N.: An Analysis of Feasible Solutions for Multi-Issue Negotiation Involving Nonlinear Utility Functions. In: Proc. of 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), pp. 1041–1048 (2009)Google Scholar
  5. 5.
    Ito, T., Hattori, H., Klein, M.: Multi-issue negotiation protocol for agents: Exploring nonlinear utility spaces. In: Proceedings of the 20th Int. Conf. on Artificial Intelligence, pp. 1347–1352 (2007)Google Scholar
  6. 6.
    Ito, T., Klein, M.: A Multi-Issue Negotiation Protocol among Competitive Agents and Its Extension to A Nonlinear Utility Negotiation Protocol. In: Proceedings of the 5th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2006), pp. 435–437 (2006)Google Scholar
  7. 7.
    Ito, T., Klein, M., Hattori, H.: A Multi-Issue Negotiation Protocol among Agents with Nonlinear Utility Functions. Multiagent and Grid Systems 4(1), 67–83 (2008)zbMATHGoogle Scholar
  8. 8.
    Klein, M., Faratin, P., Sayama, H., Bar-Yam, Y.: Negotiating Complex Contracts. Group Decision and Negotiation 12(2), 111–125 (2003)CrossRefGoogle Scholar
  9. 9.
    Narayanan, V., Jennings, N.: An Adaptive Bilateral Negotiation Model for E-Commerce Settings. In: Proceedings of the Seventh IEEE Int. Conf. on E-Commerce Technology, pp. 34–41 (2005)Google Scholar
  10. 10.
    Narayanan, V., Jennings, N.R.: Learning to Negotiate Optimally in Non-stationary Environments. In: Klusch, M., Rovatsos, M., Payne, T.R. (eds.) CIA 2006. LNCS (LNAI), vol. 4149, pp. 288–300. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Osborne, M., Rubenstein, A.: A Course in Game Theory. The MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  12. 12.
    Robu, V., Somefun, D., Poutré, J.: Modeling Complex Multi-Issue Negotiations Using Utility Graphs. In: Proceedings of the 4th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2005), p. 287 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia

Personalised recommendations