An Argumentation Framework for Deriving Qualitative Risk Sensitive Preferences

  • Wietske Visser
  • Koen V. Hindriks
  • Catholijn M. Jonker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6704)


Preferences are derived in part from knowledge. Knowledge, however, may be defeasible. We present an argumentation framework for deriving qualitative, multi-attribute preferences and incorporate defeasible reasoning about knowledge. Intuitively, preferences based on defeasible conclusions are not as strong as preferences based on certain conclusions, since defeasible conclusions may turn out not to hold. This introduces risk when such knowledge is used in practical reasoning. Typically, a risk prone attitude will result in different preferences than a risk averse attitude. In this paper we introduce qualitative strategies for deriving risk sensitive preferences.


Argumentation Framework True Attribute Inference Scheme Importance Level Prefer Extension 
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 2011

Authors and Affiliations

  • Wietske Visser
    • 1
  • Koen V. Hindriks
    • 1
  • Catholijn M. Jonker
    • 1
  1. 1.Man-Machine Interaction GroupDelft University of TechnologyThe Netherlands

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