miun.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Structure Information in Decision Trees and Similar Formalisms
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.ORCID iD: 0000-0002-0665-1889
2007 (English)In: Structure Information in Decision Trees and Similar Formalisms: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, 2007, Menlo Park, California: AAAI Press, 2007, 62-67 p.Conference paper, (Refereed)
Abstract [en]

In attempting to address real-life decision problems, where uncertainty about input data prevails, some kind of representation of imprecise information is important and several have been proposed over the years. In particular, first-order representations of imprecision, such as sets of probability measures, upper and lower probabilities, and interval probabilities and utilities of various kinds, have been suggested for enabling a better representation of the input sentences. A common problem is, however, that pure interval analyses in many cases cannot discriminate sufficiently between the various strategies under consideration, which, needless to say, is a substantial problem in real-life decision making in agents as well as decision support tools. This is one reason prohibiting a more wide-spread use. In this article we demonstrate that in many situations, the discrimination can be made much clearer by using information inherent in the decision structure. It is discussed using second-order probabilities which, even when they are implicit, add information when handling aggregations of imprecise representations, as is the case in decision trees and probabilistic networks. The important conclusion is that since structure carries information, the structure of the decision problem influences evaluations of all interval representations and is quantifiable.

Place, publisher, year, edition, pages
Menlo Park, California: AAAI Press, 2007. 62-67 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:miun:diva-4227Local ID: 4964ISBN: 978-1-57735-319-5 (print)OAI: oai:DiVA.org:miun-4227DiVA: diva2:29259
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2013-03-25Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Ekenberg, Love
By organisation
Department of Information Technology and Media
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

Total: 28 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf