Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

  • Koen V. Hindriks
  • Dmytro Tykhonov
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 44)


In automated negotiation, information gained about an opponent’s preference profile by means of learning techniques may significantly improve an agent’s negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent’s preference profile and discuss our findings.


Utility Function Learning Technique Learn Preference Negotiation Strategy Bayesian Learning 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Koen V. Hindriks
    • 1
  • Dmytro Tykhonov
    • 1
  1. 1.EEMCSDelft University of TechnologyDelftThe Netherlands

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