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
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
XED: diagnosing devices with hierarchic structure and known component failure modes
Proceedings of the sixth conference on Artificial intelligence applications
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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This paper describes an adaptive model-based diagnostic mechanism. Although model-based systems are more robust than heuristic-based expert systems, they generally require more computation time. Time consumption can be significantly reduced by using a hierarchical model scheme, which presents views of the device at several different levels of detail. We argue that in order to employ hierarchical models effectively, it is necessary to make economically rational choices concerning the trade-off between the cost of a diagnosis and its precision. The mechanism presented here makes these choices using a model diagnosability criterion which estimates how much information could be gained by using a candidate model. It takes into account several important parameters, including the level of diagnosis precision required by the user, the computational resources available, the cost of observations, and the phase of the diagnosis. Experimental results demonstrate the effectiveness of the proposed mechanism.