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An Adaptive Negotiation Strategy for Real-Time Bilateral Negotiations

  • Alexander Dirkzwager
  • Mark Hendrikx
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 535)

Abstract

Each year the Automated Negotiating Agent Competition (ANAC) introduces an increasingly complex negotiation setting to stimulate the development of negotiation strategies. This year, the competition featured a real-time bilateral negotiation setting with private reservation values and time-based discounts. This work introduces the strategy of one of the top three finalists: The Negotiator Reloaded (TNR). TNR is the first ANAC agent created using the BOA framework, a framework that allows separately developing and optimizing the components of a negotiation strategy. The agent uses a complex strategy that takes the opponent’s behavior and the domain characteristics into account. This work presents the implementation, optimization, and evaluation of the strategy.

Keywords

Automated negotiation strategy Bayesian learning Domain analysis Strategy prediction 

Notes

Acknowledgements

We would like to thank Tim Baarslag, Koen Hindriks, and Catholijn Jonker for introducing us to the field of bilateral negotiation and reviewing our paper. Furthermore, we thank the Universiteitsfonds Delft and the Interactive Intelligence Group of the Delft University of Technology for sponsoring our trip to the AAMAS 2012.

References

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Copyright information

© Springer Japan 2014

Authors and Affiliations

  1. 1.Interactive Intelligence GroupDelft University of TechnologyDelftThe Netherlands

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