Report Number: CS-TR-95-1553
Institution: Stanford University, Department of Computer Science
Title: Modeling techniques and algorithms for probabilistic model-based diagnosis and repair
Author: Srinivas, Sampath
Date: July 1995
Abstract: Model-based diagnosis centers on the use of a behavioral model of a system to infer diagnoses of anomalous behavior. For model-based diagnosis techniques to become practical, some serious problems in the modeling of uncertainty and in the tractability of uncertainty management have to be addressed. These questions include: How can we tractably generate diagnoses in large systems? Where do the prior probabilities of component failure come from when modeling a system? How do we tractably compute low-cost repair strategies? How can we do diagnosis even if only partial descriptions of device operation are available? This dissertation seeks to bring model-based diagnosis closer to being a viable technology by addressing these problems. We develop a set of tractable algorithms and modeling techniques that address each of the problems introduced above. Our approach synthesizes the techniques used in model-based diagnosis and techniques from the field of Bayesian networks.