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A Tit for Tat Negotiation Strategy for Real-Time Bilateral Negotiations

  • Tim Baarslag
  • Koen Hindriks
  • Catholijn Jonker
Part of the Studies in Computational Intelligence book series (SCI, volume 435)

Abstract

We describe the strategy of our negotiating agent, Nice Tit for Tat Agent, which reached the finals of the 2011 Automated Negotiating Agent Competition. It uses a Tit for Tat strategy to select its offers in a negotiation, i.e.: initially it cooperates with its opponent, and in the following rounds of negotiation, it responds in kind to the opponent’s actions.We give an overview of how to implement such a Tit for Tat strategy and discuss its merits in the setting of closed bilateral negotiation.

Keywords

Utility Function Acceptance Condition Bidding Strategy Acceptance Strategy Negotiation Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Axelrod, R.: The Evolution of Cooperation. Basic Books (1984)Google Scholar
  2. 2.
    Baarslag, T., Hindriks, K., Jonker, C.: Acceptance conditions in automated negotiation. In: Proceedings of The Fourth International Workshop on Agent-based Complex Automated Negotiations, ACAN 2011 (2011)Google Scholar
  3. 3.
    Baarslag, T., Hindriks, K., Jonker, C.: Towards a Quantitative Concession-Based Classification Method of Negotiation Strategies. In: Kinny, D., Hsu, J.Y.-J., Governatori, G., Ghose, A.K. (eds.) PRIMA 2011. LNCS, vol. 7047, pp. 143–158. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Hindriks, K.V., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning (2008)Google Scholar
  5. 5.
    Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human Computer Systems 48, 125–141 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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