A Multi-Agent Environment for Negotiation

  • Koen V. HindriksEmail author
  • Catholijn M. Jonker
  • Dmytro Tykhonov


In this chapter we introduce the System for Analysis of Multi-Issue Negotiation (SAMIN). SAMIN offers a negotiation environment that supports and facilitates the setup of various negotiation setups. The environment has been designed to analyse negotiation processes between human negotiators, between human and software agents, and between software agents. It offers a range of different agents, different domains, and other options useful to define a negotiation setup. The environment has been used to test and evaluate a range of negotiation strategies in various domains playing against other negotiating agents as well as humans. We discuss some of the results obtained by means of these experiments.


Multiagent System Pareto Frontier Negotiation Strategy Negotiation Protocol Negotiation Environment 
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.



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. It is part of the Pocket Negotiator project with grant number VIVI-project 08075.


  1. 1.
    Ashri, R., Rahwan, I., Luck, M.: Architectures for negotiating agents. In: The 3rd Int. Central And Eastern European Conf. on Multi-Agent Systems (2003)Google Scholar
  2. 2.
    Bosse, T., Jonker, C., Treur, J.: Experiments in human multi-issue negotiation: Analysis and support. In: Jennings,N., Sierra, C., Sonenberg, L.,Tambe, M. (eds.) Proceedings of the Third International Joint Conference on Autonomous Agents and Multi-Agent Systems, AAMAS’04, p. 672 Ű 679. IEEE Computer Society Press (2004)Google Scholar
  3. 3.
    Brazier, F., Dunin-Keplicz, B., Jennings, N.,Treur, J.:Formal specification of multi-agent systems: a real world case. International Journal of Co-operative Information Systems, IJCIS 6(1), 67–94 (1997)CrossRefGoogle Scholar
  4. 4.
    Coehoorn, R., Jennings, N.: Learningan opponent’s preferences to make effective multi-issue negotiation tradeoffs. In: Proceedings of the 6th International Conference on E-Commerce, pp. 59–68 (2004)Google Scholar
  5. 5.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Int. Journal of Robotics and Autonomous Systems 24(3-4), 159–182 (1998)CrossRefGoogle Scholar
  6. 6.
    Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make negotiation trade offs. Journal of Artificial Intelligence 142(2), 205–237 (2003)CrossRefGoogle Scholar
  7. 7.
    Fisher, R., (and for the latest edition B.Patton), W.U.: Getting to Yes: Negotiating Agreement Without Giving In.Penguin Books (1981, 1992, 2003)Google Scholar
  8. 8.
    Gode, D.K., Sunder, S.: Allocative efficiency in markets with zero intelligence (zi) traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1), 119–137 (1993)CrossRefGoogle Scholar
  9. 9.
    Ha, V., Haddawy, P.: Similarity of personal preferences: Theoretical foundations and empirical analysis. Artificial Intelligence 146(2), 149–173 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Harrenstein, P., Mahr, T., de Weerdt, M.M.: A qualitative vickrey auction. In: Endriss, U., Paul W, G. (eds.) Proceedings of the 2nd International Workshop on Computational Social Choice, pp. 289–301. University of Liverpool (2008). URL
  11. 11.
    Hindriks, K., Jonker, C.,Tykhonov, D.:Negotiation dynamics: Analysis, concession tactics, and outcomes. In: Proceedings ofthe IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’07),pp. 427–433 (2007)Google Scholar
  12. 12.
    Hindriks, K., Jonker, C.,Tykhonov, D.: Using opponent modelsfor efficient negotiation (extended abstract). In: Decker, Sichman, Sierra, Castelfranchi (eds.) Proc. of 8th Int. Conf. on Autonomous Agents and Multi agent Systems (AAMAS 2009) (2009)Google Scholar
  13. 13.
    Hindriks, K., Jonker, C.M., Tykhonov, D.: Towards an open negotiation architecture for heterogeneous agents. In: Proceedings of 12th International Workshop CIA 2007 on Cooperative Information Agents, Lecture Notes in AI. Springer-Verlag (2008)Google Scholar
  14. 14.
    Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the AAMAS 2008 (2008)Google Scholar
  15. 15.
    Hindriks, K., Tykhonov, D.: Towards aquality assessment method for learning preference profiles in negotiation. In: Proceedings of the AMEC 2008 (2008)Google Scholar
  16. 16.
    Hindriks, K.V., Tykhonov, D., de Weerdt, M.: Approximating an auction mechanism by multi-issue negotiation. In: Hindriks, K.V., Brinkman, W.P.(eds.) Proceedings of the First International Working Conference on Human Factors and Computational Models in Negotiation (HuCom 2008), pp. 33–38 (2008)Google Scholar
  17. 17.
    Jonker, C., Treur, J.: An agent architecture for multi-attribute negotiation. In: Nebel, B. (ed.) Proceedings of the 17th International Joint Conference on AI, IJCAI’01, pp. 1195 – 1201 (2001)Google Scholar
  18. 18.
    Jonker, C.M., Robu, V., Treur, J.: An agent architecture for multi-attribute negotiation using incomplete preference information. Journal of Autonomous Agents and Multi-Agent Systems 15(2), 221–252 (2007). DOI CrossRefGoogle Scholar
  19. 19.
    Klein, M., Faratin, P., Sayama, H., Bar-Yam, Y. : Negotiating complex contracts.Paper 125 of the Center for eBusines@ MIT. (2001)
  20. 20.
    Kowalczyk, R., Bui, V.: On constraint-based reasoning in e-negotiation agents.In: Dignum, F., Cortés, U. (eds.) Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems, Lecture Notes in Computer Science, pp. 31–46. Springer Ű Verlag (2003)Google Scholar
  21. 21.
    Lai, G., Sycara, K.: Ageneric frame work for automated multi-attribute negotiation.Group Decision and Negotiation 18(2), 169–187 (2009)CrossRefGoogle Scholar
  22. 22.
    Larman, C.: Applying UML and Patterns: An Introductionto Object-Oriented Analysis and Design and Iterative Development. 3edn. Prentice Hall PTR (2004)Google Scholar
  23. 23.
    Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: An automated agent for bilateral negotiation with boundedrational agents with incomplete information. In: Proc. of the 17th European Conference on Artificial Intelligence (ECAIŠ06), pp. 270–274 (2006)Google Scholar
  24. 24.
    Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artificial Intelligence Journal 172(6-7), 823–851 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Mnookin, R., Peppet, S., Tulumello, A.S.:Beyond Winning: Negotiating to Create Value in Deals and Disputes. Harvard University Press (2000)Google Scholar
  26. 26.
    Nadler, J., Thompson, L., vanBoven, L.: Learning negotiation skills: Four models of knowledge creation and transfer. Journal of Management Science 49(4), 529–540 (2003)CrossRefGoogle Scholar
  27. 27.
    Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press (1994)Google Scholar
  28. 28.
    Pruitt, D.: Negotiation Behavior. Academic Press (1981)Google Scholar
  29. 29.
    Raeesy, Z., Brzostwoski, J., Kowalczyk, R.: Towards a fuzzy-based model for human-like multi-agent negotiation. In: Proc. of the IEEE/WIC/ACM Int. Conf. on Intelligent Agent Technology, pp. 515–519 (2007)Google Scholar
  30. 30.
    Raiffa, H., Richardson, J., Metcalfe, D.: Negotiation Analysis: The Science and Art of Collaborative Decision Making. Harvard University Press (2003)Google Scholar
  31. 31.
    Sandholm, T.: Multi-agent Systems: A Modern Introduction to Distributed Artificial Intelligence, chap. Distributed rational decision making. MIT Press (1999)Google Scholar
  32. 32.
    Thompson, L.: The Heart and Mind of the Negotiator. Pearson Prentice Hall (2005)Google Scholar
  33. 33.
    Zeng, D., Sycara, K.: Benefit so flearning innegotiation. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97) (1997)Google Scholar
  34. 34.
    Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human Computer Systems 48, 125–141 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Koen V. Hindriks
    • 1
    Email author
  • Catholijn M. Jonker
    • 1
  • Dmytro Tykhonov
    • 1
  1. 1.Man-Machine Interaction group, Delft University of Technology2628CDThe Netherlands

Personalised recommendations