Readings in model-based diagnosis
Readings in model-based diagnosis
The consistency-based approach to automated diagnosis of devices
Principles of knowledge representation
An Alternative Approach to Dependeny-Recording Engines in Consistency-Based Diagnosis
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Dynamic Programming
Stacking Dynamic Time Warping for the Diagnosis of Dynamic Systems
Current Topics in Artificial Intelligence
Early fault classification in dynamic systems using case-based reasoning
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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For more than ten years different techniques have been proposed to perform model-based diagnosis of dynamic systems. Nevertheless, there is no general framework yet. Main part of the research effort has been devoted to modeling issues. Most approaches have relied upon qualitative models due to the lack of accuracy, certainty and precision in quantitative models. Hence, one question arises, is still possible to use quantitative models in the Artificial Intelligence approach to model-based diagnosis? Despite of mentioned drawbacks, quantitative models offer some advantages. If combined with pre-compiled dependency-recording, these systems avoid one of the traditional problems in the qualitative modeling approach, the feedback loop problem. These are the bases of MORDRED, a model-based diagnosis system that combines quantitative models and the possible conflict concept. This work presents results obtained in MORDRED verification and validation processes. Moreover, it analyses drawbacks found, proposed solutions, and lessons learned during the whole design and implementation cycle.