Interval Influence Diagrams

We describe a mechanism for performing probabilistic reasoning in influence diagrams us­ ing interval rather than point valued probabilities. We derive the procedures for node removal (corresponding to conditional expectation) and arc reversal (corresponding to Bayesian condi­ tioning) in influence diagrams where lower bounds on probabilities are stored at each node. The resulting bounds for the transformed diagram are shown to be optimal within the class of constraints on probability distributions which can be expressed exclusively as lower bounds on the component probabilities of the diagram. Sequences of these operations can be performed to answer probabilistic queries with indeterminacies in the input and for performing sensitivity analysis on an influence diagram. The storage requirements and computational complexity of this approach are comparable to those for point-valued probabilistic inference mechanisms, mak­ ing the approach attactive for performing sensitivity analysis and where probability information is not available. Limited empirical data on an implementation of the methodology is provided.