Diagnostic reasoning based on structure and behavior
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Utilizing knowledge-base semantics in graph-based algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
An evaluation of structural parameters for probabilistic reasoning: results on benchmark circuits
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Modeling uncertain temporal evolutions in model-based diagnosis
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
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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.