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Negowiki: A Set of Community Tools for the Consistent Comparison of Negotiation Approaches

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

Abstract

There is a number of recent research lines addressing automated complex negotiations. Most of them focus on overcoming the problems imposed by the complexity of negotiation scenarios which are computationally intractable, be it by approximating these complex scenarios with simpler ones, or by developing heuristic mechanisms to explore more efficiently the solution space. The problem with these mechanisms is that their evaluation is usually restricted to very specific negotiation scenarios, which makes very difficult to compare different approaches, to re-use concepts from previous mechanisms to create new ones or to generalize mechanisms to other scenarios. This makes the different research lines in automated negotiation to progress in an isolated manner. A solution to this recurring problem might be to create a collection of negotiation scenarios which may be used to benchmark different negotiation approaches. This paper aims to fill this gap by providing a framework for the characterization and generation of negotiation scenarios intended to address this problem. The framework has been integrated in a website, called the Negowiki, which allows to share scenarios and experiment results with the negotiation community, facilitating in this way that researchers compare and share their advancements.

Keywords

Utility Function Pareto Front Utility Space Automate Negotiation Negotiation Mechanism 
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.

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References

  1. 1.
    Buttner, R.: A classification structure for automated negotiations. In: WI-IATW 2006: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 523–530. IEEE Computer Society, Washington, DC (2006)Google Scholar
  2. 2.
    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
  3. 3.
    Fatima, S., Wooldridge, M., Jennings, N.R.: An analysis of feasible solutions for multi-issue negotiation involving nonlinear utility functions. In: AAMAS 2009: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems, pp. 1041–1048. IFAAMAS, Richland, SC (2009)Google Scholar
  4. 4.
    Fujita, K., Ito, T., Klein, M.: An approach to scalable multi-issue negotiation: Decomposing the contract space based on issue interdependencies. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 2, pp. 399–406 (2010)Google Scholar
  5. 5.
    Grabisch, M.: K-order additive discrete fuzzy measures and their representation. Fuzzy Sets Syst. 92(2), 167–189 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Hindriks, K.V., Jonker, C.M., Tykhonov, D.: A multi-agent environment for negotiation. In: El Fallah Seghrouchni, A., Dix, J., Dastani, M., Bordini, R.H. (eds.) Multi-Agent Programming, pp. 333–363. Springer, US (2009)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: Prospects, methods and challenges. International Journal of Group Decision and Negotiation 10(2), 199–215 (2001)CrossRefGoogle Scholar
  9. 9.
    Klein, M., Faratin, P., Sayama, H., Bar-Yam, Y.: Protocols for negotiating complex contracts. IEEE Intelligent Systems 18(6), 32–38 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kraus, S., Sycara, K., Evenchick, A.: Reaching agreements through argumentation: A logical model and implementation. Artificial Intelligence 1-2, 1–69 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Lai, G., Li, C., Sycara, K., Giampapa, J.: Literature review on multiattribute negotiations. Tech. Rep. CMU-RI-TR-04-66, Robotics Institute, Carnegie Mellon University, Pittsburgh, USA (December 2004)Google Scholar
  12. 12.
    Lin, R., Kraus, S., Tykhonov, D., Hindriks, K., Jonker, C.M.: Supporting the Design of General Automated Negotiators. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T., Yamaki, H. (eds.) Innovations in Agent-Based Complex Automated Negotiations. SCI, vol. 319, pp. 69–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Lopez-Carmona, M.A., Marsa-Maestre, I., de la Hoz, E., Velasco, J.R.: A region-based multi-issue negotiation protocol for non-monotonic utility spaces. Computational Intelligence (2011)Google Scholar
  14. 14.
    Manderick, B., de Weger, M., Spiessens, P.: The genetic algorithm and the structure of the fitness landscape. In: Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, pp. 1143–1150 (1991)Google Scholar
  15. 15.
    Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press, New York (1995)zbMATHGoogle Scholar
  16. 16.
    Robu, V., Somefun, D.J.A., La Poutré, J.A.: Modeling complex multi-issue negotiations using utility graphs. In: AAMAS 2005: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 280–287. ACM, New York (2005)CrossRefGoogle Scholar
  17. 17.
    Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application, pp. 3–44. Springer-Verlag New York, Inc., New York (2003)Google Scholar
  18. 18.
    Ventresca, M., Ombuki-Berman, B.: Epistasis in multi-objective evolutionary recurrent neuro-controllers. In: IEEE Symposium on Artificial Life, ALIFE 2007, pp. 77–84 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivan Marsa-Maestre
    • 1
  • Mark Klein
    • 2
  • Enrique de la Hoz
    • 1
  • Miguel A. Lopez-Carmona
    • 1
  1. 1.Computer Engineering DepartmentUniversity of AlcalaSpain
  2. 2.Center for Collective IntelligenceMassachusetts Institute of TechnologyUSA

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