Efficient Inference on Generalized Fault Diagrams

Ross D. Shachter and Leonard J. Bertrand Department of Engineering-Economic Systems, Stanford University (visiting the Center for Health Policy Research and Education, Duke University, PO Box GM, Durham, NC 27706) and Strategic Decisions Group, Menlo Park, CA for the Third Workshop on Uncertainty in Artificial Intelligence Seattle, Washington, July 10-12, 1987 The generalized fault diagram, a data structure for failure analysis based on the influence diagram, is defined. Unlike the fault tree, this structure allows for dependence among the basic events and replicated logical elements. A heuristic procedure is developed for efficient processing of these structures. Deterministic logic and conditional probabilities are both appealing frameworks in which to build a knowledge base. Each has a natural graphical representation, semantic network for logic and influence diagrams (Howard and Matheson, 1981) or bayes networks (Pearl, 1986) for probabilities.