Structure Information in Decision Trees and Similar Formalisms
2007 (English)In: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference / [ed] David Wilson & Geoff Sutcliffe, Menlo Park, California: AAAI Press , 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.
Place, publisher, year, edition, pages
Menlo Park, California: AAAI Press , 2007. 62-67 p.
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-12154ISBN: 978-1-57735-319-5OAI: oai:DiVA.org:su-12154DiVA: diva2:178674
The Twentieth International Florida Artificial Intelligence Research Society Conference. Key West, Florida, May 7–9, 2007