Efficient Methods for Multi-agent Multi-issue Negotiation: Allocating Resources

  • Mengxiao Wu
  • Mathijs de Weerdt
  • Han La Poutré
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5925)


In this paper, we present an automated multi-agent multi-issue negotiation solution to solve a resource allocation problem. We use a multilateral negotiation model, by which three agents bid sequentially in consecutive rounds till some deadline. Two issues are bundled and negotiated concurrently, so win-win opportunities can be generated as trade-offs exist between issues. We develop negotiation strategies of the agents under an incomplete information setting. The strategies are composed of a Pareto-optimal-search method and concession strategies. An important technical contribution of this paper lies in the development of the Pareto-optimal-search method for three-agent multilateral negotiation. Moreover, we present the identification of agreements and Pareto-optimal outcomes achieved by our methods in mathematical proof. We show through computer experiments that using the tractable heuristic of Pareto-optimal-search combined with well-designed concession strategies by agents results in (near) Pareto-optimal outcomes.


Utility Function Utility Level Indifference Curve Negotiation Strategy 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fatima, S., Wooldridge, M., Jennings, N.: Multi-Issue Negotiation with Deadlines. Journal of Artificial Intelligence Research 27, 381–417 (2006)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Binmore, K.: Bargaining and Coalitions. Game-Theoretic Models of Bargaining, 269–304 (1985)Google Scholar
  3. 3.
    Gerding, E., Somefun, D., La Poutre, J.: Multi-attribute Bilateral Bargaining in a One-to-Many Setting. In: Faratin, P., Rodríguez-Aguilar, J.-A. (eds.) AMEC 2004. LNCS (LNAI), vol. 3435, p. 129. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Somefun, D.J.A., Gerding, E.H., Bohte, S.M., La Poutré, J.A.H.: Automated negotiation and bundling of information goods. In: Faratin, P., Parkes, D.C., Rodríguez-Aguilar, J.-A., Walsh, W.E. (eds.) AMEC 2003. LNCS (LNAI), vol. 3048, pp. 1–17. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Somefun, D., Gerding, E., La Poutre, J.: Efficient methods for automated multi-issue negotiation: Negotiating over a two-part tariff. International Journal of Intelligent Systems 21(1) (2006)Google Scholar
  6. 6.
    Lai, G., Sycara, K., Li, C.: A decentralized model for multi-attribute negotiations with incomplete information and general utility functions. In: Proceedings of second international workshop on rational, robust, and secure negotiations in multi-agent systems. Springer, Heidelberg (2006)Google Scholar
  7. 7.
    Lai, G., Sycara, K.: A generic framework for automated multi-attribute negotiation. Group Decision and Negotiation (2009)Google Scholar
  8. 8.
    Wu, M., de Weerdt, M., La Poutré, J., Yadati, C., Zhang, Y., Witteveen, C.: Multi-player Multi-issue Negotiation with Complete Information. In: Proceedings of second international workshop on agent-based complex automated negotiations. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Rubinstein, A.: Perfect Equilibrium in a Bargaining Model. Econometrica 50(1), 97–110 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Shaked, A.: A Three-Person Unanimity Game. In: Talk given at the Los Angeles national meetings of the Institute of Management Sciences and the Operations Research Society of America (1986)Google Scholar
  11. 11.
    Fatima, S., Wooldridge, M., Jennings, N.: An agenda-based framework for multi-issue negotiation. Artificial Intelligence 152(1), 1–45 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Rausser, G., Simon, L.: A non-cooperative model of collective decision making: A multilateral bargaining approach. Department of Agricultural and Resource Economics. University of California, Berkeley (1992)Google Scholar
  13. 13.
    Faratin, P., Sierra, C., Jennings, N.: Using similarity criteria to make issue trade-offs in automated negotiations. Artificial Intelligence 142(2), 205–237 (2002)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Luo, X., Jennings, N., Shadbolt, N., Leung, H., Lee, J.: A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments. Artificial Intelligence 148(1), 53–102 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Coehoorn, R., Jennings, N.: Learning on opponent’s preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of the 6th international conference on Electronic commerce, pp. 59–68. ACM, New York (2004)CrossRefGoogle Scholar
  16. 16.
    Ros, R., Sierra, C.: A negotiation meta strategy combining trade-off and concession moves. Autonomous Agents and Multi-Agent Systems 12(2), 163–181 (2006)CrossRefGoogle Scholar
  17. 17.
    Ito, T., Hattori, H., Klein, M.: Multi-issue negotiation protocol for agents: Exploring nonlinear utility spaces. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 1347–1352 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mengxiao Wu
    • 1
  • Mathijs de Weerdt
    • 2
  • Han La Poutré
    • 1
  1. 1.Centre for Mathematics and Computer Science (CWI)AmsterdamThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands

Personalised recommendations