Structure information in decision trees and similar formalisms
2007 (English)In: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007, 2007, 62-67 p.Conference paper (Refereed)
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
2007. 62-67 p.
Formal logic, Information analysis, Knowledge representation, Probability, Problem solving
IdentifiersURN: urn:nbn:se:kth:diva-7339ISBN: 978-157735319-5OAI: oai:DiVA.org:kth-7339DiVA: diva2:12324
20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007; Key West, FL; 7 May 2007 through 9 May 2007
QC 201011172007-06-182007-06-182010-11-17Bibliographically approved