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Second-order risk constraints
KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV. Dept. of Information Technology and Media, Mid Sweden University, Sweden .
2008 (English)In: Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21, 2008, 637-642 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper discusses how numerically imprecise information can be modelled and how a risk evaluation process can be elaborated by integrating procedures for numerically imprecise probabilities and utilities. More recently, representations and methods for stating and analysing probabilities and values (utilities) with belief distributions over them (second order representations) have been suggested. In this paper, we are discussing some shortcomings in the use of the principle of maximising the expected utility and of utility theory in general, and offer remedies by the introduction of supplementary decision rules based on a concept of risk constraints taking advantage of second-order distributions.

Place, publisher, year, edition, pages
2008. 637-642 p.
Series
Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21
Keyword [en]
Artificial intelligence, Decision theory, Image segmentation, Probability, Decision rules, Expected utilities, Imprecise informations, Imprecise probabilities, Risk constraints, Risk evaluations, Second orders, Short-comings, Utility theories, Probability distributions
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-154300Scopus ID: 2-s2.0-55849085702ISBN: 9781577353652 (print)OAI: oai:DiVA.org:kth-154300DiVA: diva2:757233
Conference
21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21, 15 May 2008 through 17 May 2008, Coconut Grove, FL
Note

QC 20141021

Available from: 2014-10-21 Created: 2014-10-17 Last updated: 2014-10-21Bibliographically approved

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Output format
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