An argumentation framework for deriving qualitative risk sensitive preferences

Wietske Visser*, Koen V. Hindriks, Catholijn M. Jonker

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationModern Approaches in Applied Intelligence - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Proceedings
Pages556-565
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 25 Jul 2011
Externally publishedYes
Event24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011 - Syracuse, NY, United States
Duration: 28 Jun 20111 Jul 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6704 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011
CountryUnited States
CitySyracuse, NY
Period28/06/111/07/11

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Visser, W., Hindriks, K. V., & Jonker, C. M. (2011). An argumentation framework for deriving qualitative risk sensitive preferences. In Modern Approaches in Applied Intelligence - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Proceedings (PART 2 ed., pp. 556-565). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6704 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-21827-9_56