Skip to main content

Predicting Partners’ Behaviors in Negotiation by Using Regression Analysis

  • Conference paper
Knowledge Science, Engineering and Management (KSEM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4798))

Abstract

Prediction partners’ behaviors in negotiation has been an active research direction in recent years. By employing the estimation results, agents can modify their own ways in order to achieve an agreement much quicker or to look after much higher benefits for themselves. Some of estimation strategies have been proposed by researchers to predict agents’ behaviors, and most of them are based on machine learning mechanisms. However, when the application domains become open and dynamic, and agent relationships are complicated, it is difficult to train data which can be used to predict all potential behaviors of all agents in a multi-agent system. Furthermore because the estimation results may have errors, a single result maybe not accurate and practical enough in most situations. In order to address these issues mentioned above, we propose a power regression analysis mechanism to predict partners’ behaviors in this paper. The proposed approach is based only on the history of the offers during the current negotiation and does not require any training process in advance. This approach can not only estimate a particular behavior, but also an interval of behaviors according to an accuracy requirement. The experimental results illustrate that by employing the proposed approach, agents can gain more accurate estimation results on partners’ behaviors by comparing with other two estimation functions.

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

Access this chapter

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 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

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.

References

  1. Kraus, S.: Strategic Negotiation in Multiagent Environments. The MIT Press, Cambridge, Massachusetts (2001)

    MATH  Google Scholar 

  2. Fatima, S., Wooldridge, M., Jennings, N.: Optimal Agendas for Multi-issue Negotiation. In: AAMAS 2003. Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 129–136. ACM Press, New York (2003)

    Chapter  Google Scholar 

  3. Brzostowski, J., Kowalczyk, R.: On Possibilistic Case-Based Reasoning for Selecting Partners in Multi-agent Negotiation. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 694–705. Springer, Heidelberg (2004)

    Google Scholar 

  4. Munroe, S., Luck, M., d’Inverno, M.: Motivation-Based Selection of Negotiation Partners. In: AAMAS 2004. 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1520–1521. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  5. Parsons, S., Sierra, C., Jennings, N.: Agents that Reason and Negotiate by Arguing. Journal of Logic and Computation 8(3), 261–292 (1998)

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  7. Chajewska, U., Koller, D., Ormoneit, D.: Learning An Agent’s Utility Function by Observing Behavior. In: Proc. 18th International Conf. on Machine Learning, pp. 35–42. Morgan Kaufmann, San Francisco, CA (2001)

    Google Scholar 

  8. Faratin, P., Sierra, C., Jennings, N.: Negotiation Decision Functions for Autonomous Agents. Journal of Robotics and Autonomous Systems 24(3-4), 159–182 (1998)

    Article  Google Scholar 

  9. Fatima, S., Wooldridge, M., Jennings, N.: An Agenda-Based Framework for Multi-Issue Negotiation. Artificial Intelligence 152(1), 1–45 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  10. Schapire, R.P., McAllester, D., Littman, M., Csirik, J.: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: ICML 2002. Machine Learning, Proceedings of the Nineteenth International Conference, pp. 546–553. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  11. Gal, Y., Pfeffer, A.: Predicting Peoples Bidding Behavior in Negotiation. In: AAMAS 2006. 5th International Joint Conference on Autonomous Agents and Multiagent Systems (2006)

    Google Scholar 

  12. Brzostowski, J., Kowalczyk, R.: Predicting partner’s behaviour in agent negotiation. In: AAMAS 2006. 5th International Joint Conference on Autonomous Agents and Multiagent Systems (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Zili Zhang Jörg Siekmann

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ren, F., Zhang, M. (2007). Predicting Partners’ Behaviors in Negotiation by Using Regression Analysis. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76719-0_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76718-3

  • Online ISBN: 978-3-540-76719-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics