An improved constraint-propagation algorithm for diagnosis
Héctor Geffner, Judea Pearl · 1987
Diagnosing a system requires the identification of a set of components whose abnormal behavior could explain the faulty system behavior. Previously, model-based diagnosis schemes have proceeded through a cycle of assumptions - predictions observations assumptions-adjustment, where the basic assumptions entail the proper functioning of those components whose failure is not established. Here we propose a scheme in which every component's status is treated as a variable; therefore, predictions covering all possible behavior of the system can be generated. Remarkably, the algorithm exhibits a drastic reduction in complexity for a large family of system-models. Additionally, the intermediate computations provide useful guidance for selecting new tests. The proposed scheme may be considered as either an enhancement of the scheme proposed in [de Kleer, 1986] or an adaptation of the probabilistic propagation scheme proposed in [Pearl, 1986] for the diagnosis of deterministic systems.