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Using opponent models for efficient negotiation

Published: 10 May 2009 Publication History

Abstract

Information about the opponent is essential to improve automated negotiation strategies for bilateral multi-issue negotiation. In this paper we propose a negotiation strategy that combines a Bayesian technique to learn the preferences of an opponent during bidding and a Tit-for-Tat-like strategy to avoid exploitation by the opponent. The learned opponent model is used to achieve two important goals in negotiation. It may be used to increase the efficiency of negotiation by searching for Pareto optimal bids and to avoid exploitation by making moves that mirror the move of the other party. The performance of the proposed negotiation strategy is analyzed in a tournament setup.

References

[1]
Axelrod, R. 1984. The Evolution of Cooperation. Basic Books, Inc., Publishers, New York, USA.
[2]
Hindriks, K., Jonker, C. M., Tykhonov, D. 2007. Analysis of Negotiation Dynamics In: Proc. of CIA 2007, Delft, The Netherlands, Springer-Verlag, LNAI4676, pp. 27--35.
[3]
Hindriks, K., and Tykhonov, D. 2008. "Opponent Modelling in Automated Multi-Issue Negotiation", AAMAS'08.
[4]
Raiffa, H. 1982. The Art and Science of Negotiation, Harvard University Press.

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Information & Contributors

Information

Published In

cover image Guide Proceedings
AAMAS '09: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
May 2009
730 pages
ISBN:9780981738178

Sponsors

  • Drexel University
  • Wiley-Blackwell
  • Microsoft Research: Microsoft Research
  • Whitestein Technologies
  • European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
  • The Foundation for Intelligent Physical Agents

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 10 May 2009

Author Tags

  1. Bayesian learning
  2. Tit-for-Tat
  3. automated multi-issue negotiation
  4. negotiation strategy
  5. opponent modelling

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  • Research-article

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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