Marc Le Goc, Emilie Masse, Corinne Curt


This paper presents a modelling approach of dynamic process for diagnosis that is compatible with the Stochastic Approach framework for discovering temporal knowledge from the timed observations contained in a database. The motivation is to define a multi-model formalism that is able to represent both the knowledge of these two sources. The aim is to model the process at the same level of abstraction that an expert uses to diagnose the process. The underlying idea is that at this level of abstraction, the model is simple enough to allow an efficient diagnosis. The proposed formalism represents the knowledge in four models: a structural model defining the components and the connection relations of the process, a behavioural model defining the states and the transitions states of the process, a functional model containing the logical relations between the values of the process’s variables, which are defined in the perception model. The models are linked with the process’s variables. This point facilitates the analysis of the consistency of the four models and is the basis of the corresponding knowledge modelling methodology. The formalism and the methodology is illustrated with the model of a hydraulic dam of Cublize (France).


  1. Abu-Hanna, A., Benjamins, R., and Jansweijer, W. (1991). Device understanding and modeling for diagnosis. IEEE Expert, 6(2):26-31.
  2. Basseville, M. and Cordier, M.-O. (1996). Surveillance et diagnostic de systèmes dynamiques : approche complémentaire du traitement de signal et de l'intelligence artificielle. Rapport.
  3. Chittaro, L., Guida, G., Tasso, C., and Toppano, E. (1993). Functional and teological knowledge in the multimodeling approach for reasoning about physical systems: A case study in diagnosis. IEEE Transactions on systems.
  4. Clancey, W. (1987). Heuristic classification. Artificial Intelligence, 25(3):289-350.
  5. Dague, P. (2001). Théorie logique du diagnostic à base de modèles. Diagnostic, Intelligence Artificielle et Reconnaissance des Formes, pages 17-105.
  6. Davis, R. (1984). Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347-410.
  7. De Kleer, J. and Brown, J. (1984). A Qualitative Physics Confluences. Artificial Intelligence, 24:7-83.
  8. Falkeihainer, B. and Forbus, K. D. (1988). Seting up large scale qualitative models. In Proceedings of the 7th National Conference of Artificial Intelligence, pages 301-306, St. Paul, MN, USA.
  9. Falkeihainer, B. and Forbus, K. D. (1991). Compositional modeling: Finding the right model for the job. Artificial Intelligence, 51:95-143.
  10. Forbus, K. et al. (1984). Qualitative Process Theory. Artificial Intelligence, 24(1-3):85-168.
  11. Franke, D. W. (1991). Deriving and using descriptions of purpose. IEEE Expert, 6(2):41-47.
  12. Hayes, P. (1989). Naive physics I: ontology for liquids. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA.
  13. Le Goc, M. (2004). Sachem, a real time intelligent diagnosis system based on the discrete event paradigm. Simulation, The Society for Modeling and Simulation International Ed., 80(11):591-617.
  14. Le Goc, M. (2006). Notion d'Observation pour le Diagnostic des Processus Dynamiques: Application à Sachem et à la découverte de Connaissances Temporelles. Hdr, Faculté des Sciences et Techniques de Saint Jéroˆme, Marseille, France.
  15. Le Goc, M. and Bénayadi, N. (2008). Dicovering expert's knowledge from sequences of discrete event class occurrences. In To appear in the proceedings of the 10th International Conference on Enterprise Information Systems (ICEIS08), Barcelona, Spain, 12-16th of June 2008.
  16. Le Goc, M., Bouché, P., and Giambiasi, N. (2005). Stochastic modeling of continuous time discrete event sequence for diagnosis. 16th International Workshop on Principles of Diagnosis (DX'05) Pacific Grove, California, USA.
  17. Lee, W. and Kuipers, B. (1988). Non-Intersection of Trajectories in Qualitative Phase Space: A Global Constraint for Qualitative Simulation. Proceedings of the Seventh National Conference on Artificial Intelligence, AAAI88, pages 286-291.
  18. Masse, E. and Le Goc, M. (2007). Modeling dynamic systems from their behavior for a multi model based diagnosis. In Proceedings of the 18th International Workshop on the Principles of Diagnosis (DX'07), Nashville, TN, USA, May 29-31 2007.
  19. Murthy, S. (1988). Qualitative reasoning at multiple resolutions. Proceedings of the National Conference on Artificial Intelligence, pages 296-300.
  20. Reiter, R. (1987). A theory for diagnosis for first principles. Artificial Intelligence, 32:57-95.
  21. Rosenberg, R. and Karnopp, D. (1983). Introduction to Physical System Dynamics. McGraw-Hill, Inc. New York, NY, USA.
  22. Schreiber, A., Akkermans, J. M., Anjewierden, A. A., de Hogg, R., Shadbolt, N. R., Van de Velde, W., and Wielinga, B. J. (2000). Knowledge Engineering and Management, The CommonKADS Methodology. MIT Press.
  23. Struss, P. and Dressler, O. (1989). Physical negation - integrating fault models into the general diagnostic engine. In Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI-89), pages 1318-1323.
  24. Thetiot, R. (1999). Utilisation de l'Approche Multimodèles pour l'Aide au Diagnostic d'Installations Industrielles. Phd in sciences, Université d'Evry Val d'Essonne, France.
  25. Zanni, C., Le Goc, M., and Frydman, C. (2005). A conceptual framework for the analysis, classification and choice of knowledge-based diagnosis systems.
  26. Zouaoui, F., Thétiot, R., and Dumas, M. (1997). Multimodeling representation of pwr primary coolant loop. In Proceedings Poster of International Joint Conference on Artificial Intelligence (IJCAI-97), page 116.

Paper Citation

in Harvard Style

Le Goc M., Masse E. and Curt C. (2008). MODELING PROCESSES FROM TIMED OBSERVATIONS . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8111-51-7, pages 249-256. DOI: 10.5220/0001884502490256

in Bibtex Style

author={Marc Le Goc and Emilie Masse and Corinne Curt},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,},

in EndNote Style

JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,
SN - 978-989-8111-51-7
AU - Le Goc M.
AU - Masse E.
AU - Curt C.
PY - 2008
SP - 249
EP - 256
DO - 10.5220/0001884502490256