Skip to main content
SpringerLink
Log in

MDS: An Integrated Architecture for Associational and Model-Based Diagnosis

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper discusses the design and implementation of an integrated diagnosis system, MDS (Multi-level Diagnosis System), which combines associational and model-based approaches to diagnosis. The design and implementation of the associational module is tailored to achieving efficiency in routine diagnostic problem solving, and to providing a desirable interface for the users. The model-based diagnosis module is developed to achieve completeness and consistency in the fault isolation task, and to avoid the brittleness that often occurs in associational systems. MDS addresses the important issue of combining the use of “deeper” knowledge in the form of a system model with “shallow” (or associational) knowledge, using a diagnostic controller to improve completeness and consistency without sacrificing efficiency. The diagnostic controller also employs a methodology for automated knowledge refinement by identifying incomplete and inconsistent rules and diagnostic tests in the associational module, and then by performing updates to correct the problems. This paper focuses on the design and implementation of the diagnostic controller.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. R. Davis and W.C. Hamscher, “Model-based reasoning: Troubleshooting,” in Exploring Artificial Intelligence: Survey Talks from the National Conferences on AI, edited by H.E. Shrobe, Morgan Kaufmann: San Mateo, CA, pp. 297-346, 1988.

    Google Scholar 

  2. R. Davis, “Form and content in model based reasoning,” in Proceedings of Workshop on Model Based Reasoning, IJCAI-89, Detroit, MI, 1989, pp. 11-33.

  3. T. Bylander and B. Chandrasekaran, “Generic tasks for knowledge-based reasoning: The 'right' level of abstraction for knowledge acquisition,” International Journal of Man-Machine Studies, vol. 26, pp. 231-243, 1987.

    Google Scholar 

  4. B. Falkenhainer and K. Forbus, “Compositional modeling: Finding the right model for the job,” AI Journal, vol. 51, pp. 95-143, 1991.

    Google Scholar 

  5. J. deKleer, “Focusing on probable diagnoses,” in Proceedings of the 9th AAAI, 1991, pp. 842-848.

  6. S. Weiss, C. Kulikowsi, and A. Safir, “A model-based consultation system for the long-term management of glaucoma,” in Proceedings of the 5th IJCAI, 1977, pp. 826-832.

  7. G. Biswas and X. Yu, “A formal modeling scheme for continuous systems: Focus on diagnosis,” in Proceedings of IJCAI, Chambery, France, 1993, pp. 1474-1479.

  8. G. Biswas, R. Kapadia, and X.W. Yu, “Combined qualitativequantitative steady state diagnosis of continuous-valued systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 27, no. 2, March 1997.

  9. X.W. Yu, “Multi-level reasoning and diagnosis,” Ph.D. Dissertation, Computer Science Department, Vanderbilt University, December, 1992.

  10. E.H. Shortliffe, Computer-based Medical Consultations: MYCIN, Elsevier/North Holland, New York, 1976.

    Google Scholar 

  11. Y. Peng and J.A. Reggia, “Plausibility of diagnostic hypothesis: The nature of simplicity,” AAAI-86, 1986, pp. 140-145.

  12. J. de Kleer and B.C. Williams, “Diagnosing multiple faults,” Artificial Intelligence, vol. 32, pp. 97-130, 1987.

    Google Scholar 

  13. D.Dvorak and B.Kuipers, “Model-based monitoring of dynamic systems,” in Proceedings of 11th IJCAI, 1989, pp. 1238-1243.

  14. P. Struss and O. Dressler, “Physical negation: Integrating fault models into the general diagnostic engine,” in Proceedings of IJCAI-89, Detroit, MI, 1989, pp. 1318-1323.

  15. M. Gallanti, M. Roncato, R. Stefanini, and G. Tornielli, “A diagnostic algorithm based on models at different levels of abstraction,” in Proceedings of 11th IJCAI, 1989, pp. 1350-1355.

  16. F. Lackinger and W. Nejdl, “Diamon: A model-based troubleshooter based on qualitative reasoning,” IEEE Expert, vol. 8, no. 1, pp. 33-40, 1993.

    Google Scholar 

  17. L. Palowitch, “Fault diagnosis of process plants using causal models,” Ph.D. Thesis, Massachussets Inst. ofTechnology, 1996.

  18. B.J. Glass, W.K. Erickson, and K.J. Swanson, “TEXSYS: A large-scale demonstration of model-based real-time control of a space stations subsystem,” in Proceedings of the Seventh Conference on Artificial Intelligence Applications, 1991, pp. 378-384.

  19. P. Dague, O. Raiman, and P. Deves, “Troubleshooting: When modeling is the difficulty,” in Proceedings of 6th National Conference on Artificial Intelligence, Seattle,WA, August, 1987, pp. 600-605.

  20. P.J. Mosterman and G. Biswas, “Diagnosis of continuous valued systems in transient operating regions,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 29, no. 6, pp. 554-565, 1999.

    Google Scholar 

  21. P.K. Fink and J.C. Lusth, “Expert systems and diagnostic expertise in the mechanical and electrical domains,” IEEE Trans. on Systems, Man, and Cybernetics, vol. SMC-17, pp. 340-349, 1987.

    Google Scholar 

  22. R. Canton, F. Pipitone, W. Lander, and M. Marrone, “Modelbased probabilistic reasoning for electronics troubleshooting,” in Proceedings of 8th IJCAI, 1983, pp. 207-211.

  23. R.S. Patil, “Causal representation of patient illness for electrolyte and acid-based diagnosis,” Technical Report, no. 267, Massachussetts Institute of Technology, Laboratory for Computer Science, Cambridge, MA, 1981.

    Google Scholar 

  24. G.W. Rosenwald and C. Liu, “Rule-based system validation through automatic identification of equivalence classes,” IEEE MDS: An Integrated Architecture 195 pp. 24-31, 1997.

    Google Scholar 

  25. Y. Huang and R. Miles, “Using case-based techniques to enhance constraint satisfaction problem solving,” Applied Arti-ficial Intelligence, an International Journal, vol. 10, no. 4, 1996.

  26. R.C. Rosenberg and D.C. Karnopp, Introduction to Physical System Dynamics, McGraw-Hill, New York, 1983.

    Google Scholar 

  27. L. Portinale and P. Torasso, “ADAPtER: An integrated diagnostic system combining case-based and abductive reasoning,” Topics in Case Based Reasoning, Proceedings of the First International Conference on Case-Based Reasoning, 1995, pp. 277-288.

  28. M.H. Sqalli and E.C. Feuder, “Diagnosing interoperability problems by enhancing constraint satisfaction with case-based reasoning,” Ninth International Work on Principles of Diagnosis, 1998, pp. 266-273.

  29. G. Biswas and T.S. Anand, “Using the Dempster Shafer scheme in a mixed-initiative expert system shell,” in Uncertainty in Arti-ficial Intelligence, edited by L. Kand, T.S. Levitt, and J. Lenamer, Elsevier Science, New York, pp. 223-239, 1989.

    Google Scholar 

  30. G. Guida and G. Mauri, “Evaluatiing performance and quality of knowledge-based systems: Foundation and methodology,” IEEE Transaction on Knowledge and Data Engineering, vol. 5, no. 2, pp. 204-224, 1993.

    Google Scholar 

  31. S. Craw and R. Boswell, “Representing problem-solving for knowledge refinement,” in Proceedings of the Sixteenth National Conference on Artificial Intelligence Eleventh Innovative Applications of AI Conference, 1999.

  32. D. Ourston and R.J. Mooney, “Theory refinement combining analytical and empirical methods,” Artificial Intelligence, vol. 66, pp. 273-309, 1994.

    Google Scholar 

  33. M. Tallis and Y. Gil, “Designing scripts to guide users in modifying knowledge-based systems,” in Proceedings of the Sixteenth National Conference on Artificial Intelligence Eleventh Innovative Applications of AI Conference, 1999.

  34. P.M. Murphy and M.J. Pazzani, “Revision of production system rule-bases,” Machine Learning: Proceedings of the 11th International Conference, 1994, pp. 199-207.

  35. J.R. Quinlan, C4.5-Programs for Machine Learning, Morgan Kaufmann: CA, 1993.

    Google Scholar 

  36. R.S. Michalski, I. Mozetic, J. Hong, and N. Lavrac, “Multipurpose incremental learning system AQ15 and its testing application to three medical domains,” in Proceedings of the Fifth National Conference on Artificial Intelligence, 1986, pp. 1041-1045.

  37. S. Tsumoto and H. Tanaka, “Automated discovery of medical expert system rules from clinical database on rough sets,” in Proceedings of conference on Data mining Applications, 1997, pp. 63-69.

  38. C. Tudorie, C. Segal, and E. Pecheanu, “Human operator observations elicitation in a hybrid (neuro-symbolical) Diagnosis,” in Proceedings of the Ninth International Workshop on Principles of Diagnosis, 1998, pp. 282-285.

  39. M. Someren, J. Surma, and P. Torasso, “Autility-based approach to learning in a mixed case-based and model-based architecture,” in Proceedings of the Second International Conference on Case Based Reasoning, 1997.

Download references

Author information

Authors and Affiliations

  1. Department of CS, SIUE, Edwardsville, IL, 62026, USA

    Xudong W. Yu & Jerry Weinberg

  2. Department of EECS, Vanderbilt University, Nashville, TN, 37235, USA

    Gautam Biswas

Authors
  1. Xudong W. Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gautam Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jerry Weinberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu, X.W., Biswas, G. & Weinberg, J. MDS: An Integrated Architecture for Associational and Model-Based Diagnosis. Applied Intelligence 14, 179–195 (2001). https://doi.org/10.1023/A:1008318126645

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008318126645

Search

Navigation