Abstract
This paper describes an approach to selecting appropriate causal models for engineering diagnosis. We have chosen a hybrid approach which is a combination of model composition and model reuse. Model composition permits reasoning with multiple models that contain explicit assumptions. Difficulties related to intractability during model composition are reduced by model reuse. We are currently validating and testing the system on full-scale civil engineering structures.
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References
P. Nayak, L. Joskowicz, “Efficient compositional modelling for generating causal explanations”, Artificial Intelligence, vol. 83, 1996.
B. Falkenhainer, K. D. Forbus, “Compositional modelling: Finding the right model for the job”, Artificial Intelligence, vol. 51, pp. 95–143, 1991.
G.Guida, A.Stefanini, Industrial application of knowledge based diagnosis, Elsevier Science, Amsterdam, 1992.
M. Sanseverino, F.Cascio, “Model-based diagnosis for automotive repair”, IEEE Expert/Intelligent systems and their applications, November/December, 1997.
Michel Manago & Eric Auriol, “Integrating Induction and Case-Based Reasoning for Troubleshooting CFM-56 Aircraft Engines”, Proceedings, Third German CBR Workshop, Kaiserslautern, 1995.
J.R. Trott and B. Leng, An Engineering Approach for Troubleshooting Case Bases, Proceedings, ICCBR-97, 1997.
M. J. Chantler, G. M. Coghill, Q. Shen, R. R. Leitch, “Selecting tools and techniques for model-based diagnosis”, Artificial Intelligence in Engineering, vol. 12, pp. 81–98, 1998.
J. L. Kolodner, “Case-based Reasoning”, Morgan Kaufmann, San Mateo, CA, 1993.
D.Leake, “Case-Based Reasoning: Experiences, Lessons, & Future Directions”, AAAI Press / MIT Press, Menlo Park, CA., 1996.
R.Bergmann, G.Pews, W.Wilke, Explanation-based similarity: A unifying approach for integrating domain knowledge into case-based reasoning for diagnosis and planning tasks, Topics in case-based reasoning, First European workshop, EWCBR-93, Kaiserslautern, Germany, Springer-Verlag, 1993.
L.K.Branting, “Integrating cases and models through approximate-model-based adaptation”, Proceedings, Multimodal Reasoning, AAAI Symposium, AAAI Press, pp.1–5, 1998.
L. Portinale, P. Torasso, “Performance issues in ADAPTER, a combined CBR-MBR diagnostic architecture, Multimodal Reasoning, AAAI Symposium, AAAI Press, pp.47–52, 1998.
M. Someren, J. Surma, and P. Torasso, “A utility-based approach to learning in a mixed case-based and model-based architecture”, Proceedings, Second International Conference on Case-Based Reasoning (ICCBR-97), Rhode Island, July 25–27, pp. 477–489, 1997.
J. Rousu and R. J. Aarts, “Model-based reasoning about cases”, Proceedings, Multimodal Reasoning, AAAI Symposium, AAAI Press, pp.6–9, 1998.
C. Bach and D. Allemang, “Case-based reasoning in diagnostic expert systems”, AI Communications, vol. 9, pp. 49–52, 1996.
M.P. Feret, J.I. Glasgow, Case-based reasoning in model-based diagnosis, Applications of artificial intelligence in engineering, vol. 7, pp. 679–692, 1992.
A. Goel, A model-based approach to case adaptation, Proceedings, 13th annual conference of the cognitive science society, (CogSci'91), pp. 143–148, 1991.
B. Raphael and B. Kumar, “Indexing and retrieval of cases in a case-based design system”, AI EDAM, vol. 10, pp. 47–63, 1996.
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© 1998 Springer-Verlag Berlin Heidelberg
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Raphael, B., Smith, I. (1998). Case-based model selection for engineering diagnosis. In: Smyth, B., Cunningham, P. (eds) Advances in Case-Based Reasoning. EWCBR 1998. Lecture Notes in Computer Science, vol 1488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056326
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DOI: https://doi.org/10.1007/BFb0056326
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