Skip to main content

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

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make negotiation trade-offs. Artif. Intell. 142(2), 205–237 (2003)

    Article  MathSciNet  Google Scholar 

  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. Hindriks, K., Tykhonov, D.: Opponent modeling in automated multi-issue negotiation using Bayesian learning. In: AAMAS’08, 331–338 (2008)

    Google Scholar 

  4. Brzostowski, J., Kowalczyk, R.: Predicting partner’s behaviour in agent negotiation. In: AAMAS ’06, 355–361 (2006)

    Google Scholar 

  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. 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. Zeng, D., Sycara, K.: Bayesian learning in negotiation. Int. J. Hum. Comput. Syst. 48, 125–141 (1998)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianye Hao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Japan

About this chapter

Cite this chapter

Hao, J., Leung, Hf. (2014). CUHKAgent: An Adaptive Negotiation Strategy for Bilateral Negotiations over Multiple Items. In: Marsa-Maestre, I., Lopez-Carmona, M., Ito, T., Zhang, M., Bai, Q., Fujita, K. (eds) Novel Insights in Agent-based Complex Automated Negotiation. Studies in Computational Intelligence, vol 535. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54758-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-54758-7_11

  • Published:

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-54757-0

  • Online ISBN: 978-4-431-54758-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics