Advertisement

An Adaptive Bilateral Negotiation Model Based on Bayesian Learning

  • Chao Yu
  • Fenghui Ren
  • Minjie Zhang
Part of the Studies in Computational Intelligence book series (SCI, volume 435)

Abstract

Endowing the negotiation agent with a learning ability such that a more beneficial agreement might be obtained is increasingly gaining attention in agent negotiation research community. In this paper, we propose a novel bilateral negotiation model based on Bayesian learning to enable self-interested agents to adapt negotiation strategies dynamically during the negotiation process. Specifically, we assume that two agents negotiate over a single issue based on time-dependent tactic. The learning agent has a belief about the probability distribution of its opponent’s negotiation parameters (i.e., the deadline and reservation offer). By observing opponent’s historical offers and comparing them with the fitted offers derived from a regression analysis, the agent can revise its belief using the Bayesian updating rule and can correspondingly adapt its concession strategy to benefit itself. By being evaluated empirically, this model shows its effectiveness for the agent to learn the possible range of its opponent’s private information and alter its concession strategy adaptively, as a result a better negotiation outcome can be achieved.

Keywords

Negotiation Process Reserve Price Bayesian Learning Learning Agent Negotiation Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sun, T., Zhu, Q., Xia, Y., Cao, F.: A Bilateral Price Negotiation Strategy Based on Bayesian Classification and Q-learning. Journal of Information & Computational Science 13, 2773–2780 (2011)Google Scholar
  2. 2.
    Ng, S., Sulaiman, M., Selamat, M.: Intelligent negotiation agents in electronic commerce applications. Journal of Artificial Intelligence 2, 29–39 (2009)CrossRefGoogle Scholar
  3. 3.
    Ng, S., Sulaiman, M., Selamat, M.: Machine learning approach in optimizing negotiation agents for E-commerce. Information Technology Journal 8, 801–810 (2009)CrossRefGoogle Scholar
  4. 4.
    Aydogan, R., Yolum, P.: Ontology-based learning for negotiation. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 02, pp. 177–184. IEEE Computer Society (2008)Google Scholar
  5. 5.
    Hindriks, K., Tykhonov, D.: Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning. In: Proceedings of the 7th International Conference on Autonomous Agents and Multi Agent Systems, AAMAS 2008, pp. 331–338. IFAAMAS, Richland (2008)Google Scholar
  6. 6.
    Ren, F., Zhang, M.-J.: Predicting Partners’ Behaviors in Negotiation by Using Regression Analysis. In: Zhang, Z., Siekmann, J.H. (eds.) KSEM 2007. LNCS (LNAI), vol. 4798, pp. 165–176. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Ren, F., Zhang, M.: Prediction of Partners’ Behaviors in Agent Negotiation under Open and Dynamic Environments. In: 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops, pp. 379–382. IEEE (2007)Google Scholar
  8. 8.
    Aydogan, R., Yolum, P.: Learning consumer preferences using semantic similarity. In: Proceedings of Sixth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2007, pp. 1293–1300. ACM, New York (2007)Google Scholar
  9. 9.
    Ramchurn, S.D., Sierra, C., Godo, L., Jennings, N.R.: Negotiating using rewards. Artificial Intelligence 171, 805–837 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Bzostowski, J., Kowalczyk, R.: Predicting partner’s behavior in agent negotiation. In: Proceedings of 5th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2006, pp. 355–361. IFAAMAS, Richland (2006)CrossRefGoogle Scholar
  11. 11.
    Narayanan, V., Jennings, N.R.: Learning to Negotiate Optimally in Non-stationary Environments. In: Klusch, M., Rovatsos, M., Payne, T.R. (eds.) CIA 2006. LNCS (LNAI), vol. 4149, pp. 288–300. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Narayanan, V., Jennings, N.R.: An adaptive bilateral negotiation model for e-commerce settings. In: Seventh IEEE International Conference on E-Commerce Technology, pp. 34–41. IEEE (2005)Google Scholar
  13. 13.
    Li, J., Cao, Y.D.: Bayesian learning in bilateral multi-issue negotiation and its application in MAS-based electronic commerce. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2004, pp. 437–440 (2004)Google Scholar
  14. 14.
    Soo, V.W., Hung, C.A.: On-line incremental learning in bilateral multi-issue negotiation. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, pp. 314–315. ACM, New York (2002)CrossRefGoogle Scholar
  15. 15.
    Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24, 159–182 (1998)CrossRefGoogle Scholar
  16. 16.
    Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human-Computers Studies 48, 125–141 (1998)CrossRefGoogle Scholar
  17. 17.
    Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringUniversity of WollongongWollongongAustralia

Personalised recommendations