Abstract
Model-based Diagnosis allows the identification of the parts which fail in a system. The models are based on the knowledge of the system to diagnose, and can be represented by constraints associated to components. Inputs and outputs of components are represented as variables of those constraints, and they can be observable and non-observable depending on the situation of sensors. In order to obtain the minimal diagnosis in a system, an important issue is to find out the possible minimal conflicts in an efficient way.
In this work, we propose a new approach to automate and to improve the determination of possible minimal conflict sets. This approach has two phases. In the first phase, we determine components clusters in the system in order to reduce drastically the number of contexts to consider. In the second phase, we construct a reduced context network with the possible minimal conflicts. In this phase we use Gröbner bases reduction. A novel logical architecture of Constraint Databases is used to store the model, the components clusters and possible minimal conflict sets. The necessary information in each phase is obtained by using a standard query language.
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Gómez-López, M.T., Ceballos, R., Gasca, R.M., Pozo, S. (2004). Determination of Possible Minimal Conflict Sets Using Constraint Databases Technology and Clustering. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_94
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DOI: https://doi.org/10.1007/978-3-540-30498-2_94
Publisher Name: Springer, Berlin, Heidelberg
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