Decision Making in Qualitative Influence Diagrams

The increasing number of knowledge-based systems that build on a Bayesian belief network or influence diagram acknowledge the usefulness of these frameworks for addressing complex real-life problems. The usually large number of probabilities and utilities required for their application, however, is often considered a major obstacle. The use of qualitative abstractions may to some extent remove this obstacle. Qualitative belief networks and associated algorithms have been developed before. In this paper, we address qualitative influence diagrams and outline an efficient algorithm for qualitative decision making.