Skip to main content

An Innovative Approach for Predicting Both Negotiation Deadline and Utility in Multi-issue Negotiation

  • Conference paper
PRICAI 2014: Trends in Artificial Intelligence (PRICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8862))

Included in the following conference series:

  • 6545 Accesses

Abstract

In agent negotiation, agents usually need to know their opponents’ negotiation parameters (i.e preference, deadline, reservation utility) to effectively adjust their negotiation strategies, thus an agreement can be reached. However, in a competitive negotiation environment, agents may not be willing to reveal their negotiation parameters, which increases the difficulty of reaching an agreement. In order to solve this problem, agents need to have the learning ability to predict their opponents’ negotiation parameters. In this paper, a Bayesian-based prediction approach is proposed to help an agent to predict its opponent’s negotiation deadline and reservation utility in bilateral multi-issue negotiation. Besides, a concession strategy adjustment algorithm is integrated into the proposed prediction approach to improve the negotiation result. The experimental results indicate that the proposed approach can increase the profit and the success rate of bilateral multi-issue negotiation.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Coehoorn, R.M., Jennings, N.R.: Learning on opponent’s preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of the 6th International Conference on Electronic Commerce, pp. 59–68 (2004)

    Google Scholar 

  2. Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Robotics and Autonomous Systems 24(3), 159–182 (1998)

    Article  Google Scholar 

  3. Gwak, J., Sim, K.M.: Bayesian learning based negotiation agents for supporting negotiation with incomplete information. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 1, pp. 163–168 (2011)

    Google Scholar 

  4. Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M.J., Sierra, C.: Automated negotiation: prospects, methods and challenges. Group Decision and Negotiation 10(2), 199–215 (2001)

    Article  Google Scholar 

  5. Luo, X., Miao, C., Jennings, N.R., He, M., Shen, Z., Zhang, M.: Kemnad: A knowledge engineering methodology for negotiating agent development. Computational Intelligence 28(1), 51–105 (2012)

    Article  MathSciNet  Google Scholar 

  6. Pan, L., Luo, X., Meng, X., Miao, C., He, M., Guo, X.: A two-stage win-win multiattribute negotiation model: Optimization and then concession. Computational Intelligence 29(4), 577–626 (2013)

    Article  MathSciNet  Google Scholar 

  7. Raiffa, H.: The art and science of negotiation. Harvard University Press (1982)

    Google Scholar 

  8. Ros, R., Sierra, C.: A negotiation meta strategy combining trade-off and concession moves. Autonomous Agents and Multi-Agent Systems 12(2), 163–181 (2006)

    Article  Google Scholar 

  9. Sim, K.M., Guo, Y., Shi, B.: Adaptive bargaining agents that negotiate optimally and rapidly. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1007–1014 (2007)

    Google Scholar 

  10. Sim, K.M., Guo, Y., Shi, B.: Blgan: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information. IEEE Transactions on Systems, Man, and Cybernetics, Part B 39(1), 198–211 (2009)

    Article  Google Scholar 

  11. Soo, V.-W., Hung, C.-A.: On-line incremental learning in bilateral multi-issue negotiation. In: Proceedings of the First International Conference on Autonomous Agents and Multi-Agent Systems, Part 1, pp. 314–315 (2002)

    Google Scholar 

  12. Williams, C.R., Robu, V., Gerding, E.H., Jennings, N.R.: An overview of the results and insights from the third automated negotiating agents competition (ANAC 2012), vol. 535, pp. 151–162 (2014)

    Google Scholar 

  13. Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human-Computer Studies 48(1), 125–141 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, J., Ren, F., Zhang, M. (2014). An Innovative Approach for Predicting Both Negotiation Deadline and Utility in Multi-issue Negotiation. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_93

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13560-1_93

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13559-5

  • Online ISBN: 978-3-319-13560-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics