Advertisement

Consensus Policy Based Multi-agent Negotiation

  • Enrique de la Hoz
  • Miguel A. Lopez-Carmona
  • Mark Klein
  • Ivan Marsa-Maestre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)

Abstract

Multiagent negotiation may be understood as a consensus based group decision-making which ideally should seek the agreement of all the participants. However, there exist situations where an unanimous agreement is not possible or simply the rules imposed by the system do not seek such unanimous agreement. In this paper we propose to use a consensus policy based mediation framework (CPMF) to perform multiagent negotiations. This proposal fills a gap in the literature where protocols are in most cases indirectly biased to search for a quorum. The mechanisms proposed to perform the exploration of the negotiation space are derived from the Generalized Pattern Search non-linear optimization technique (GPS). The mediation mechanisms are guided by the aggregation of the agent preferences on the set of alternatives the mediator proposes in each negotiation round. Considerable interest is focused on the implementation of the mediation rules where we allow for a linguistic description of the type of agreements needed. We show empirically that CPMF efficiently manages negotiations following predefined consensus policies and solves situations where unanimous agreements are not viable.

Keywords

Ordered Weight Average Negotiation Protocol Ordered Weight Average Operator Unanimous Agreement Ordered Weight Average 
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.
    Ehtamo, H., Hamalainen, R.P., Heiskanen, P., Teich, J., Verkama, M., Zionts, S.: Generating pareto solutions in a two-party setting: constraint proposal methods. Management Science 45(12), 1697–1709 (1999)CrossRefzbMATHGoogle Scholar
  2. 2.
    Heiskanen, P., Ehtamo, H., Hamalainen, R.P.: Constraint proposal method for computing pareto solutions in multi-party negotiations. European Journal of Operational Research 133(1), 44–61 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Ito, T., Klein, M., Hattori, H.: A multi-issue negotiation protocol among agents with nonlinear utility functions. Journal of Multiagent and Grid Systems 4(1), 67–83 (2008)CrossRefzbMATHGoogle Scholar
  4. 4.
    Klein, M., Faratin, P., Sayama, H., Bar-Yam, Y.: Protocols for negotiating complex contracts. IEEE Intelligent Systems 18(6), 32–38 (2003)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lai, G., Sycara, K.: A generic framework for automated multi-attribute negotiation. Group Decision and Negotiation 18, 169–187 (2009)CrossRefGoogle Scholar
  6. 6.
    Lewis, R.M., Torczon, V., Trosset, M.W.: Direct search methods: then and now. Journal of Computational and Applied Mathematics 124, 191–207 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Li, M., Vo, Q.B., Kowalczyk, R.: Searching for fair joint gains in agent-based negotiation. In: Decker, Sichman, Sierra, Castelfranchi (eds.) Proc. of 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), Budapest, Hungary, May, 10-15, pp. 1049–1056 (2009)Google Scholar
  8. 8.
    Lopez-Carmona, M.A., Marsa-Maestre, I., De La Hoz, E., Velasco, J.R.: A region-based multi-issue negotiation protocol for nonmonotonic utility spaces. Computational Intelligence 27(2), 166–217 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Lopez-Carmona, M.A., Marsa-Maestre, I., Ibanez, G., Carral, J.A., Velasco, J.R.: Improving trade-offs in automated bilateral negotiations for expressive and inexpressive scenarios. Journal of Intelligent & Fuzzy Systems 21, 165–174 (2010)Google Scholar
  10. 10.
    Lopez-Carmona, M.A., Marsa-Maestre, I., Klein, M., Ito, T.: Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces. Journal of Autonomous Agents and Multiagent Systems, 1–51 (2010)Google Scholar
  11. 11.
    Marsa-Maestre, I., Lopez-Carmona, M.A., Velasco, J.R., Ito, T., Klein, M., Fujita, K.: Balancing utility and deal probability for auction-based negotiations in highly nonlinear utility spaces. In: 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), Pasadena, California, USA, pp. 214–219 (July 2009)Google Scholar
  12. 12.
    Vo, Q.B., Padgham, L., Cavedon, L.: Negotiating flexible agreements by combining distributive and integrative negotiation. Intelligent Decision Technologies 1(1-2), 33–47 (2007)CrossRefGoogle Scholar
  13. 13.
    Yager, R.: Quantifier guided aggregation using owa operators. International Journal of Intelligent Systems 11, 49–73 (1996)CrossRefGoogle Scholar
  14. 14.
    Yager, R., Kacprzyk, J.: The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer (1997)Google Scholar
  15. 15.
    Zadeh, L.: A computational approach to fuzzy quantifiers in natural languages. Computing and Mathematics with Applications 9, 149–184 (1983)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Enrique de la Hoz
    • 1
  • Miguel A. Lopez-Carmona
    • 1
  • Mark Klein
    • 2
  • Ivan Marsa-Maestre
    • 1
  1. 1.Computer Engineering DepartmentUniversidad de Alcala Escuela PolitecnicaMadridSpain
  2. 2.Center for Collective IntelligenceMIT Sloan School of Management, Massachusetts Institute of TechnologyUSA

Personalised recommendations