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We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure Bayesian network to represent diagnostic cases, with nodes representing issues, causes, and symptoms. Dirichlet distributions are assessed at knowledge acquisition time to indicate the strength of relationships between variables. During a diagnosis session, a relevant subnetwork is extracted from a Bayesian-network database that describes adoi:10.1109/3468.541343 fatcat:cyoye456kfbblheioxngekur7a