IBERAMIA 2004: Advances in Artificial Intelligence – IBERAMIA 2004 pp 942-952 | Cite as
Determination of Possible Minimal Conflict Sets Using Constraint Databases Technology and Clustering
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.
Keywords
Relevant Context Context Network Component Cluster Logical Architecture Polynomial ConstraintPreview
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References
- 1.Davis, R.: Diagnostic reasoning based on structure and behavior. Artificial Intelligence 24, 347–410 (1984)CrossRefGoogle Scholar
- 2.Reiter, R.: A theory of diagnosis from first principles. Artificial Intelligence 32(1), 57–96 (1987)MATHCrossRefMathSciNetGoogle Scholar
- 3.Kleer, J.D., Mackworth, A., Reiter, R.: Characterizing diagnoses and systems. Artificial Intelligence 56(2-3), 197–222 (1992)MATHCrossRefMathSciNetGoogle Scholar
- 4.Goldin, D., Kanellakis, P.: Constraint query algebras constraints. Journal E. F. editor (1996)Google Scholar
- 5.Kanellakis, P.C., Kuper, G.M., Revesz, P.Z.: Constraint query languages. In: Symposium on Principles of Database Systems, pp. 299–313 (1990)Google Scholar
- 6.Revesz, P.: Introduction to Constraint Databases. Springer, Heidelberg (2001)Google Scholar
- 7.Kleer, J.D., Williams, B.: Diagnosing multiple faults. Art. Int (1987)Google Scholar
- 8.Kleer, J.D.: An assumption-based truth maintenance system. Artificial Intelligence 28(2), 127–161 (1986)CrossRefGoogle Scholar
- 9.Buchberger, B.: Gröbner bases: An algorithmic method in polynomial ideal theory. In: Bose, N.K. (ed.) Multidimensional Systems Theory, pp. 184–232 (1985)Google Scholar
- 10.de la Banda, M.G., Stuckey, P., Wazny, J.: Finding all minimal unsatisfiable subsets. In: Proc. Of the 5th ACM Sigplan Internacional (2003)Google Scholar
- 11.Frisk, E.: Residual generator design for non-linear, polynomial systems - a gröbner basis approach. In: Proc. IFAC Safeprocess, Budapest (2000)Google Scholar
- 12.Gasca, R., Valle, C.D., Ceballos, R., Toro, M.: An integration of fdi and dx approaches to polinomial models. In: 14th International Workshop on principles of Diagnosis - DX (2003)Google Scholar
- 13.Pulido, J.: Posibles conflictos como alternativa al registro de dependencias en línea para el diagnóstico de sistemas continuos. PhD. degree, Universidad de Valladolid (2000)Google Scholar
- 14.Guernez, C., Petitot, M., Cassar, J., Staroswiecki, M.: Fault detection and isolation on non linear polynomial systems. In: 15th IMACS World Congress (1997)Google Scholar