Abstract
We present explanation-based learning (EBL) methods aimed at improving the performance of diagnosis systems integrating associational and model-based components. We consider multiple-fault model-based diagnosis (MBD) systems and describe two learning architectures. One, EBLIA, is a method for “learning in advance.” The other, EBL(p), is a method for “learning while doing.” EBLIA precompiles models into associations and relies only on the associations during diagnosis. EBL(p) performs compilation during diagnosis whenever reliance on previously learned associational rules results in unsatisfactory performance—as defined by a given performance threshold p. We present results of empirical studies comparing MBD without learning versus EBLIA and EBL(p). The main conclusions are as follows. EBLIA is superior when it is feasible, but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Davis, R. (1989). Form and content in a model-based reasoning. In E. Searl (Ed.), Proceedings of the Workshop on Model Based Reasoning (pp. 11–27). Detroit, MI: Boeing Computer Services.
Davis, R., & Hamscher, W. (1988). Model-based reasoning: Troubleshooting. In H.E. Shrobe (Ed.), Exploring Artificial Intelligence, chapter 8 (pp. 297–346). San Mateo, CA: Morgan Kaufmann.
de Kleer, J. (1976). Local methods for localizing faults in electronic circuits (AI Memo 394). Cambridge, MA: MIT Artificial Intelligence Laboratory.
de Kleer, J. (1986). Problem solving with the ATMS. Artificial Intelligence, 28, 197–224.
de Kleer, J. (1990). Exploiting locality in a TMS. In Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 264–271). Boston, MA: AAAI Press/The MIT Press.
de Kleer, J. (1991). Focusing on probable diagnoses. In Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 842–848). Anaheim, CA: AAAI Press/The MIT Press.
de Kleer, J., Mackworth, A., & Reiter, R. (1992). Characterizing diagnoses and systems. Artificial Intelligence, 56, 197–222.
de Kleer, J., & Williams, B.C. (1987). Diagnosing multiple faults. Artificial Intelligence, 32, 97–130.
de Velde, W.V. (1988). Quality of learning. In Proceedings of the Eighth European Conference on Artificial Intelligence (pp. 408–413). London: Pitman.
DeJong, G.F., & Mooney, R. (1986). Explanation-based learning: An alternative view. Machine Learning, 1 (2), 145–176.
El Fattah, Y., & O'Rorke, P. (1991a). Learning multiple fault diagnosis. In Proceedings of the Seventh IEEE Conference on Artificial Intelligence Applications (pp. 235–239). Los Alamitos, CA: IEEE Computer Society Press.
El Fattah, Y., & O'Rorke, P. (1991b). On tractability and learning in model based diagnosis. In Proceedings of the Workshop on Model Based Reasoning. Anaheim, CA: AAAI.
El Fattah, Y., & O'Rorke, P. (1991c). The role of compilation in constraint based reasoning. In Working Notes of the AAAI Spring Symposium on Constraint Based Reasoning (pp. 225–241). Stanford, CA: AAAI.
El Fattah, Y., & O'Rorke, P. (1992). Learning approximate diagnosis. In Proceedings of the Eighth IEEE Conference on Artificial Intelligence Applications (pp. 150–156). Los Alamitos, CA: IEEE Computer Society Press.
Etzioni, O. (1990). Why PRODIGY/EBL works. In Proceedings of the Eighth National Conference on Artificial Intelligence (pp. 916–922). Menlo Park, CA: AAAI Press/The MIT Press.
Feigenbaum, E.A. (1979). Themes and case studies of knowledge engineering. In D. Michie (Ed.), Expert systems in the micro electronic age (pp. 3–25). Edinburgh: Edinburgh University Press.
Friedrich, G., Gottlob, G., & Nejdl, W. (1990). Generating efficient diagnostic procedures from model-based knowledge using logic programming techniques. Computers Mathematical Applications, 20 (9/10), 57–72.
Garey, M., & Johnson, D. (1979). Computers and Tractability. New York: W.H. Freeman and Company.
Goel, A.K. (1991). Knowledge compilation: A symposium. IEEE Expert, 6 (2), 71–93.
Greiner, R., Smith, B.A., & Wilkerson, R. (1989). A correction to the algorithm in Reiter's theory of diagnosis. Artificial Intelligence, 41, 79–88.
Kean, A., & Tsiknis, G. (1990). An incremental method for generating prime implicants/implicates. Journal of Symbolic Computation, 9, 185–206.
Keller, R.M. (1990). In defense of compilation: A response to Davis' “Form and content in model-based reasoning.” In E. Scarl, (Ed.), Proceedings of the Workshop on Model Based Reasoning (pp. 22–31). Boston, MA: Boeing Computer Services.
Keller, R.M. (1991). Applying knowledge compilation techniques to model-based reasoning. IEEE Expert, 6 (2), 82–87.
Koseki, Y. (1989). Experience learning in model-based diagnosis. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 1356–1362). Detroit, MI: Morgan Kaufmann.
Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In Proceedings of the Seventh National Conference on Artificial Intelligence (pp. 564–569). St. Paul, MN: Morgan Kaufmann.
Mitchell, T.M., Keller, R.M., & Kedar-Cabelli, S.T. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1 (1), 47–80.
O'Rorke, P. (1989). LT revisited: Explanation-based learning and the logic of Principia Mathematica. Machine Learning, 4 (2), 117–159.
Reiter, R. (1987). A theory of diagnosis from first principles. Artificial Intelligence, 32, 57–95.
Reiter, R., & de Kleer, J. (1987). Foundations of assumption-based truth maintenance systems. In Proceedings of the Sixth National Conference on Artificial Intelligence (pp. 183–188). Seattle, WA: Morgan Kaufmann.
Resnick, P. (1989). Generalizing on multiple grounds: Performance learning in model-based troubleshooting (Technical Report AI—TR 1052). Cambridge, MA: MIT Artificial Intelligence Laboratory.
Zercher, K. (1988). Model-based learning of rules for error diagnosis. In W. Hoeppner (Ed.), Proceedings of the German Workshop on Artificial Intelligence (pp. 196–205). Berlin: Springer Verlag.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
El Fattah, Y., O'Rorke, P. Explanation-Based Learning for Diagnosis. Machine Learning 13, 35–70 (1993). https://doi.org/10.1023/A:1022631512320
Issue Date:
DOI: https://doi.org/10.1023/A:1022631512320