Structure Information in Decision Trees and Similar Formalisms

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 sec...

• Type: Conference
• Year: 2007
• Where: FLAIRS
• Authors: Mats Danielson, Love Ekenberg, David Sundgren
• Keywords: Artificial Intelligence, Decision Problem, FLAIRS 2007, Real-life Decision, Real-life Decision Problems