Detecting Unsuccessful Automated Negotiation Threads When Opponents Employ Hybrid Strategies

  • Ioannis Papaioannou
  • Ioanna Roussaki
  • Miltiades Anagnostou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5227)

Abstract

In artificial intelligence systems, building agents that negotiate on behalf of their owners aiming to maximise their utility is a quite challenging research field. In this paper, such agents are enhanced with techniques based on neural networks (NNs) to predict their opponents’ hybrid negotiation behaviour, thus achieving more profitable results. The NNs are used to early detect the cases where agreements are not achievable, supporting the decision of the agents to withdraw or not from the negotiation threads. The designed NN-assisted negotiation strategies have been evaluated via extensive experiments and are proven to be very useful.

Keywords

Automated negotiations MLP & GR neural networks NN-assisted negotiation strategies Opponent behaviour prediction Early detection of unsuccessful negotiations Hybrid negotiation strategies 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kraus, S.: Automated Negotiation and Decision Making in Multiagent Environments. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, pp. 150–172. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Fasli, M., Shehory, O. (eds.): TADA/AMEC 2006. LNCS (LNAI), vol. 4452. Springer, Heidelberg (2007)Google Scholar
  3. 3.
    Lomuscio, A., Wooldridge, M., Jennings, N.: A Classification Scheme for Negotiation in Electronic Commerce. J. of Group Decision and Negotiation 12(1), 31–56 (2003)CrossRefGoogle Scholar
  4. 4.
    Rosenschein, J., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, Cambridge (1994)Google Scholar
  5. 5.
    Muller, H.: Negotiation principles. In: O’Hare, G., Jennings, N. (eds.) Foundations of Distributed Artificial Intelligence, pp. 211–229. John Wiley & Sons, New York (1996)Google Scholar
  6. 6.
    Li, H., Su, S., Lam, H.: On Automated E-Business Negotiations: Goal, Policy, Strategy, and Plans of Decision and Action. J. of Organizational Computing and Electronic Commerce 16(1), 1–29 (2006)CrossRefGoogle Scholar
  7. 7.
    Faratin, P., Sierra, C., Jennings, N.J.: Negotiation Decision Functions for Autonomous Agents. Int. J. of Robotics and Autonomous Systems 24(3-4), 159–182 (1998)CrossRefGoogle Scholar
  8. 8.
    Fei, Y., Wang, G.: Research on an Intelligent On-Line Negotiation System. In: IEEE International Conference on e-Business Engineering, pp. 722–728. IEEE Press, New York (2005)Google Scholar
  9. 9.
    Cranor, L.F., Resnick, P.: Protocols for Automated Negotiations with Buyer Anonymity and Seller Reputations. NETNOMICS Journal 2(1), 1–23 (2000)CrossRefGoogle Scholar
  10. 10.
    Brzostowski, J., Kowalczyk, R.: Predicting Partner’s Bahaviour in Agent Negotiation. In: 5th International Joint Conference on Autonomous Agents and Multi-Agent Systems, Session Argumentation and Negotiation, pp. 355–361. ACM, New York (2006)Google Scholar
  11. 11.
    Brzostowski, J., Kowalczyk, R.: Adaptive Negotiation with Online Prediction of Opponent Behaviour in Agent-based Negotiation. In: 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 263–269. IEEE Computer Society, Washington (2006)CrossRefGoogle Scholar
  12. 12.
    Raiffa, H.: The Art and Science of Negotiation. Harvard University Press, Cambridge (1982)Google Scholar
  13. 13.
    Pruitt, D.: Negotiation Behavior. Academic Press Inc., New York (1981)Google Scholar
  14. 14.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, London (1999)MATHGoogle Scholar
  15. 15.
    Rau, H., Tsai, M., Chen, C., Shiang, W.: Learning-based Automated Negotiation between Shipper and Forwarder. J. of Computers and Industrial Engineering 51(3), 464–481 (2006)CrossRefGoogle Scholar
  16. 16.
    Carbonneau, R., Kersten, G., Vahidov, R.: Predicting Opponent’s Moves in Electronic Negotiations Using Neural Networks. In: International Conference of Group Decision and Negotiation (GDN 2006), Karlsruhe, Germany (2006)Google Scholar
  17. 17.
    Zeng, Z.M., Meng, B., Zeng, Y.Y.: An Adaptive Learning Method in Automated Negotiation. In: Yeung, D.S., Liu, Z.-Q., Wang, X.-Z., Yan, H. (eds.) ICMLC 2005. LNCS (LNAI), vol. 3930. Springer, Heidelberg (2006)Google Scholar
  18. 18.
    Oprea, M.: The Use of Adaptive Negotiation in Agent-Mediated Electronic Commerce. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 594–605. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Roussaki, I., Papaioannou, I., Anagnostou, M.: Employing Neural Networks to Assist Negotiating Intelligent Agents. In: 2nd IEE International Conference on Intelligent Environ-ments 2006 (IE 2006), Athens, Greece (2006)Google Scholar
  20. 20.
    Papaioannou, I., Roussaki, I., Anagnostou, M.: Comparing the Performance of MLP and RBF Neural Networks Employed by Negotiating Intelligent Agents. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006), Hong Kong, China (2006)Google Scholar
  21. 21.
    Roussaki, I., Papaioannou, I., Anagnostou, M.: Building Automated Negotiation Strategies Enhanced by MLP and GR Neural Networks for Opponent Agent Behaviour Prognosis. In: Sandoval, F., Gonzalez Prieto, A., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 152–161. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ioannis Papaioannou
    • 1
  • Ioanna Roussaki
    • 1
  • Miltiades Anagnostou
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece

Personalised recommendations