Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and Conceders

  • Victor Sanchez-AnguixEmail author
  • Reyhan Aydoğan
  • Vicente Julian
  • Catholijn M. Jonker
Part of the Studies in Computational Intelligence book series (SCI, volume 535)


Under some circumstances, a group of individuals may need to negotiate together as a negotiation team against another party. Unlike bilateral negotiation between two individuals, this type of negotiations entails to adopt an intra-team strategy for negotiation teams in order to make team decisions and accordingly negotiate with the opponent. It is crucial to be able to negotiate successfully with heterogeneous opponents since opponents’ negotiation strategy and behavior may vary in an open environment. While one opponent might collaborate and concede over time, another may not be inclined to concede. This paper analyzes the performance of recently proposed intra-team strategies for negotiation teams against different categories of opponents: competitors, matchers, and conceders. Furthermore, it provides an extension of the negotiation tool Genius for negotiation teams in bilateral settings. Consequently, this work facilitates research in negotiation teams.


Agreement technologies Collective decision making Negotiation teams 



One part of this research is supported by TIN2011-27652-C03-01 and TIN2012-36586-C03-01 of the Spanish government. Other part of this research is supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic Affairs; the Pocket Negotiator project with grant number VICI-project 08075 and the New Governance Models for Next Generation Infrastructures project with NGI grant number 04.17. We would also like to thank Tim Baarslag due to his helpful and valuable comments and feedback about Genius.


  1. 1.
    An, B., Sim, K., Tang, L., Li, S., et al.: Continuous-time negotiation mechanism for software agents. IEEE Trans. Syst. Man Cybern. B Cybern. 36(6), 1261–1272 (2006)CrossRefGoogle Scholar
  2. 2.
    Baarslag, T., Hindriks, K., Jonker, C., Kraus, S., & Lin, R.: The first automated negotiating agents competition (ANAC 2010). In: New Trends in Agent-Based Complex Automated Negotiations, pp. 113–135. Springer Berlin Heidelberg (2012)Google Scholar
  3. 3.
    Baarslag, T., Hindriks, K. V., Jonker, C.M.: Towards a quantitative concession-based classification method of negotiation strategies. In: Agents in Principle, Agents in Practice. Lecture Notes of The 14th International Conference on Principles and Practice of Multi-Agent Systems (2011)Google Scholar
  4. 4.
    Baarslag, T., Hindriks, K.V., Jonker, C.M.: A Tit for Tat Negotiation Strategy for Real-Time Bilateral Negotiations. Studies in Computational Intelligence, vol. 435, pp. 229–233. Springer, Berlin (2013)Google Scholar
  5. 5.
    Brodt, S., Thompson, L.: Negotiating teams: a levels of analysis. Group Dyn. 5(3), 208–219 (2001)CrossRefGoogle Scholar
  6. 6.
    Ehtamo, H., Kettunen, E., Hamalainen, R.P.: Searching for joint gains in multi-party negotiations. Eur. J. Oper. Res. 130(1), 54–69 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Int. J. Rob. Auton. Syst. 24(3–4), 159–182 (1998)CrossRefGoogle Scholar
  8. 8.
    Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make issue trade-offs in automated negotiations. Artif. Intell. 142, 205–237 (2002)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Fujita, K., Ito, T., Klein, M.: Secure and efficient protocols for multiple interdependent issues negotiation. J. Intell. Fuzzy Syst. 21(3), 175–185 (2010)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Hindriks, K., Jonker, C., Tykhonov, D.: The benefits of opponent models in negotiation. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp. 439–444 (2009)Google Scholar
  11. 11.
    Kawaguchi, S., Fujita, K., Ito, T.: Compromising strategy based on estimated maximum utility for automated negotiation agents competition. In: Modern Approaches in Applied Intelligence, vol. 6704, pp. 501–510. Springer, Berlin (2011)Google Scholar
  12. 12.
    Klein, M., Faratin, P., Sayama, H., Bar-Yam, Y.: Negotiating complex contracts. Group Decis. Negot. 12(2), 111–125 (2003)CrossRefGoogle Scholar
  13. 13.
    Lai, G., Sycara, K., Li, C.: A decentralized model for automated multi-attribute negotiations with incomplete information and general utility functions. Multiagent Grid Syst. 4(1), 45–65 (2008)zbMATHGoogle Scholar
  14. 14.
    Lin, R., Kraus, S., Baarslag, T., Tykhonov, D., Hindriks, K., Jonker, C.M.: Genius: an integrated environment for supporting the design of generic automated negotiators. Comput. Intell. (2012)Google Scholar
  15. 15.
    Mansour, K., Kowalczyk, R.: A meta-strategy for coordinating of one-to-many negotiation over multiple issues. In: Foundations of Intelligent Systems, vol. 122, pp. 343–353. Springer, Berlin (2012)Google Scholar
  16. 16.
    Marsa-Maestre, I., Lopez-Carmona, M.A., Velasco, J.R., de la Hoz, E.: Effective bidding and deal identification for negotiations in highly nonlinear scenarios. In: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’09), pp. 1057–1064. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2009)Google Scholar
  17. 17.
    Marsa-Maestre, I., López-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: International Joint Conference on Artificial Intelligence, pp. 214–219 (2009)Google Scholar
  18. 18.
    Nguyen, T., Jennings, N.: Coordinating multiple concurrent negotiations. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1064–1071. IEEE Computer Society, Washington, DC (2004)Google Scholar
  19. 19.
    Nurmi, H.: Voting systems for social choice. In: Handbook of Group Decision and Negotiation, pp. 167–182. Springer Netherlands (2010)Google Scholar
  20. 20.
    Robu, V., La Poutré, J.A.: Retrieving the structure of utility graphs used in multi-item negotiation through collaborative filtering of aggregate buyer preferences. In: Rational, Robust and Secure Negotiations. Computational Intelligence, vol. 89. Springer, Berlin (2008)Google Scholar
  21. 21.
    Robu, V., Somefun, D.J.A., La Poutré, J.A.: Modeling complex multi-issue negotiations using utility graphs. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS’05), pp. 280–287. ACM, New York (2005)Google Scholar
  22. 22.
    Sanchez-Anguix, V., Julian, V., Botti, V., Garc/’ia-Fornes, A.: Analyzing intra-team strategies for agent-based negotiation teams. In: 10th International Conference on Autonomous Agents and Multiagent Systems, pp. 929–936 (2011)Google Scholar
  23. 23.
    Sanchez-Anguix, V., Dai, T., Semnani-Azad, Z., Sycara, K., Botti, V.: Modeling power distance and individualism/collectivism in negotiation team dynamics. In: 45 Hawaii International Conference on System Sciences (HICSS-45), pp. 628–637 (2012)Google Scholar
  24. 24.
    Sanchez-Anguix, V., Julian, V., Botti, V., García-Fornes, A.: Reaching unanimous agreements within agent-based negotiation teams with linear and monotonic utility functions. IEEE Trans. Syst. Man Cybern. B Cybern. 42(3), 778–792 (2012)CrossRefGoogle Scholar
  25. 25.
    Sandholm, T.: An implementation of the contract net protocol based on marginal cost calculations. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 256–262. AAAI Press, Menlo Park (1993)Google Scholar
  26. 26.
    Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge (2009)Google Scholar
  27. 27.
    Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Trans. Comput. 100(12), 1104–1113 (1980)CrossRefGoogle Scholar
  28. 28.
    Sánchez-Anguix, V., Julian, V., Botti, V., García-Fornes, A.: Studying the impact of negotiation environments on negotiation teams’ performance. Inf. Sci. 219, 17–40 (2013)CrossRefGoogle Scholar
  29. 29.
    Tambe, M., Jung, H.: The benefits of arguing in a team. AI Mag. 20, 85–92 (1999)Google Scholar
  30. 30.
    Thompson, L., Peterson, E., Brodt, S.: Team negotiation: an examination of integrative and distributive bargaining. J. Pers. Soc. Psychol. 70, 66–78 (1996)CrossRefGoogle Scholar
  31. 31.
    van Galen Last, N.: Agent Smith: Opponent Model Estimation in Bilateral Multi-issue Negotiation. In: New Trends in Agent-Based Complex Automated Negotiations, pp. 167–174. Springer Berlin Heidelberg (2012)Google Scholar
  32. 32.
    Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: Iamhaggler: a negotiation agent for complex environments. In: New Trends in Agent-based Complex Automated Negotiations, pp. 151–158. Springer Berlin Heidelberg (2012)Google Scholar
  33. 33.
    Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: Negotiating concurrently with unknown opponents in complex, real-time domains. In: 20th European Conference on Artificial Intelligence, 242, pp. 834–839 (2012)Google Scholar

Copyright information

© Springer Japan 2014

Authors and Affiliations

  • Victor Sanchez-Anguix
    • 1
    Email author
  • Reyhan Aydoğan
    • 2
  • Vicente Julian
    • 1
  • Catholijn M. Jonker
    • 2
  1. 1.Departamento de Sistemas Informáticos y ComputaciónUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Interactive Intelligence GroupDelft University of TechnologyDelftThe Netherlands

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