Abstract
For more than ten years different techniques have been pro- posed to perform model-based diagnosis of dynamic systems. Neverthe- less, 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 mod- els 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 comb- ines quantitative models and the possible conflict concept. This work presents results obtained in MORDRED verification and validation pro- cesses. Moreover, it analyses drawbacks found, proposed solutions, and lessons learned during the whole design and implementation cycle.
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Pulido, B., Alonso, C., Acebes, F. (2001). Lessons Learned from Diagnosing Dynamic Systems Using Possible Conflicts and Quantitative Models. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_17
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