Negotiation Exploiting Reasoning by Projections

  • Toni Mancini
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 55)


We present a framework that allows two self-motivated distributed agents to perform, in an efficient way, negotiations aiming at achieving mutually satisfactory agreements, when privacy of information is an issue, and no central authority could be used. In particular, each agent has her own constraints to satisfy, as well as her own utility function. Such issues are kept private, and cannot be disclosed to the counterpart. Negotiation is hence carried out by exchanging proposals and by performing sophisticated forms of reasoning on the remote agent’s offers, by trying to infer some characteristics of the counterpart, in order to achieve efficient process convergence.


Utility Function Negotiation Process Local Agent Business Process Management Texture Area 
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.
    Cadoli, M.: Proposal-based negotiation in convex regions. In: Klusch, M., Omicini, A., Ossowski, S., Laamanen, H. (eds.) CIA 2003. LNCS, vol. 2782, pp. 93–108. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Cadoli, M., Chella, G., Mancini, T.: iAgree: a system for proposal-based negotiation among intelligent agents. In: Proc. of ECAI 2006. IOS Press, Amsterdam (2006) System demonstrationGoogle Scholar
  3. 3.
    Conry, S.E., Kuwabara, K., Lesser, V.R., Meyer, R.A.: Multistage negotiation for distributed constraint satisfaction. IEEE Trans. on Systems, Man and Cybernetics 21(6), 462–477 (1991)CrossRefGoogle Scholar
  4. 4.
    Costantini, S., Tocchio, A., Tsintza, P.: Experimental evaluation of a heuristic approach for P2P negotiation. In: Proc. of RCRA 2007 (2007)Google Scholar
  5. 5.
    Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make issue trade-offs in automated negotiations. Artif. Intell. 142(2), 205–237 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Fatima, S.S., Wooldridge, M., Jennings, N.R.: Multi-issue negotiation with deadlines. J. of Artif. Intell. Research 27, 381–417 (2006)MathSciNetGoogle Scholar
  7. 7.
    Ito, T., Hattori, H., Klein, M.: Multi-issue negotiation protocol for agents: Exploring nonlinear utility spaces. In: Proc. of IJCAI 2007, pp. 1347–1352. Morgan Kaufmann, San Francisco (2007)Google Scholar
  8. 8.
    Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Wooldridge, M., Sierra, C.: Automated negotiation, prospects, methods and challenges. Group Dec. and Negot. 10, 199–215 (2001)CrossRefGoogle Scholar
  9. 9.
    Jennings, N.R., Norman, T.J., Faratin, P., O’Brien, P., Odgers, B.: Autonomous agents for business process management. Applied Artif. Intell. 14(2), 145–189 (2000)CrossRefGoogle Scholar
  10. 10.
    Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artif. Intell. 172(6–7), 823–851 (2008)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Sycara, K.P., Roth, S., Sadeh, N., Fox, M.: Distributed constrained heuristic search. IEEE Trans. on Systems, Man and Cybernetics 21(6), 446–461 (1991)CrossRefGoogle Scholar
  12. 12.
    Yokoo, M., Katsutoshi, H.: Algorithms for distributed constraint satisfaction: A review. Autonomous Agents and Multi-Agents Systems 3(2), 185–207 (2000)CrossRefGoogle Scholar
  13. 13.
    Zlotkin, G., Rosenschein, J.S.: Mechanisms for automated negotiation in state oriented domains. J. of Artif. Intell. Research 5, 163–238 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Toni Mancini
    • 1
  1. 1.Dipartimento di InformaticaUniversità di Roma “La Sapienza”Italy

Personalised recommendations