Abstract
The main idea of Consistency-Based Diagnosis rests in generation of diagnostic hypotheses stating which components of the system may be faulty, so that assuming them faulty explains the observations in a consistent way. Such diagnostic process is analyzed from qualitative perspective. Qualitative diagnostic inference, qualitative conflicts and qualitative diagnoses are presented in detail. Finally, we examine how qualitative knowledge can contribute to refinement of diagnostic inference and how compilation of diagnostic knowledge can be approached.
Research carried out within AGH University of Science and Technology statutory research 18.18.120.059.
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Notes
- 1.
Of the author and his former Ph.D. student Barłomiej Górny, and some common work with Prof. Jan Maciej Kościelny.
- 2.
In Model-Based Diagnosis it is typically assumed that faulty behavior is caused by a fault of a named component or a simultaneous fault of a set of such components; no faults caused by faulty links, parameter setting or the internal structure are considered.
- 3.
Recall that we consider only minimal conflict sets and minimal diagnoses. In the other case, conflicts such as \(\{m1(-),m2(-), a1(+) \}\) would also be possible, but removing a single element such as \(m2(-)\) would not lead to regaining consistency.
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Ligęza, A. (2016). Towards Knowledge Compilation for Automated Diagnosis: A Qualitative, Model-Based Approach with Constraint Programming. In: Kowalczuk, Z. (eds) Advanced and Intelligent Computations in Diagnosis and Control. Advances in Intelligent Systems and Computing, vol 386. Springer, Cham. https://doi.org/10.1007/978-3-319-23180-8_26
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