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CUHKAgent: An Adaptive Negotiation Strategy for Bilateral Negotiations over Multiple Items

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 535)

Abstract

Automated negotiation techniques can greatly improve the negotiation efficiency and quality of our human being, and a lot of automated negotiation strategies and mechanisms have been proposed in different negotiation scenarios until now. To achieve efficient negotiation, there are two major challenges we are usually faced with: how to model and predict the strategy and preference of the opponent. To this end we propose an adaptive negotiating strategy (CUHKAgent) to predict the opponent’s strategy and preference at a high level, and make informed decision accordingly.

Keywords

Adaption Negotiation Reinforcement learning 

References

  1. 1.
    Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make negotiation trade-offs. Artif. Intell. 142(2), 205–237 (2003)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Saha, S., Biswas, A., Sen, S.: Modeling opponent decision in repeated one-shot negotiations. In: AAMAS’05, pp. 397–403 (2005)Google Scholar
  3. 3.
    Hindriks, K., Tykhonov, D.: Opponent modeling in automated multi-issue negotiation using Bayesian learning. In: AAMAS’08, 331–338 (2008)Google Scholar
  4. 4.
    Brzostowski, J., Kowalczyk, R.: Predicting partner’s behaviour in agent negotiation. In: AAMAS ’06, 355–361 (2006)Google Scholar
  5. 5.
    Hao, J.Y., Leung, H.F.: An efficient negotiation protocol to achieve socially optimal allocation. In: PRIMA’12, 46–60 (2012)Google Scholar
  6. 6.
    Zeng, D., Sycara, K.: Bayesian learning in negotiation. In: AAAI Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems, pp. 99–104 (1996)Google Scholar
  7. 7.
    Zeng, D., Sycara, K.: Bayesian learning in negotiation. Int. J. Hum. Comput. Syst. 48, 125–141 (1998)CrossRefGoogle Scholar
  8. 8.
    Coehoorn, R.M., Jennings, N.R.: Learning an opponent’s preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of ICEC’04, ACM Press, 59–68 (2004)Google Scholar
  9. 9.
    Baarslag, T., Fujita, K., Gerding, E.H., Hindriks, K., Ito, T., Jennings, N.R., Jonker, C., Kraus, S., Lin, R., Robu, V., Williams, C.R.: Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artif. Intell. 198, 73–103 (2013)CrossRefGoogle Scholar
  10. 10.
    Hao, J.Y., Leung, H.F.: Abines: an adaptive bilateral negotiating strategy over multiple items. In: Proceedings of IAT’12, vol. 2, pp. 95–102 (2012)Google Scholar
  11. 11.
    Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)MATHGoogle Scholar

Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina

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