Abstract
This paper presents a system which learns a diagnostic knowledge base using a-priori knowledge and a set of examples. The a-priori knowledge consists of a causal model of the domain and a body of phenomenological theory, describing the links between abstract concepts and their possible manifestations in the world. The phenomenological knowledge is used deductively, the causal model is used abductively and the examples are used inductively. The system has been applied to learn the knowledge base of a diagnostic expert system for mechanical trouble-shooting.
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References
F. Bergadano, A. Giordana, L. Saitta (1990), “Automated versus Manual Knowledge Acquisition: A Comparison in a Real Domain”, Proc. First Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (Tokyo, Japan), pp. 301–314.
F. Bergadano, A. Giordana, L. Saitta (1988a), “Automated Concept Acquisition in Noisy Environment”, IEEE Trans. PAMI, PAMI-10, 555–575.
F. Bergadano, A. Giordana (1988b), “A Knowledge Intensive Approach to Concept Induction”, Proc. Machine Learning Conference (Ann Arbor, MI), pp. 305–317.
M. Botta, L. Saitta (1988), “Improving Knowledge Base System Performances by Experience”, Proc. EWSL-88 (Glasgow, UK), pp. 15–23.
M. Botta, A, Giordana, L. Saitta (1990), “Knowledge Base Refinement using a Causal Model”, Intelligent Systems: State of the Art and Future Trends, Z. Ras & M. Zemankova (Eds.), Ellis-Horwood Publ. Co.
M. Botta, S. Ravotto, L. Saitta (in press), “Use of Causal Models and Abduction in Learning Diagnostic Knowledge”, Int. J. of Man-Machine Studies, in press.
L. Console, P. Torasso, D. Theseider Dupré (1990), “A Completion Semantics for Object-Level Abduction”, Proc. AAAI Symposium on Automated Abduction (Stanford, CA), pp. 72–75.
R. Davis (1984), “Diagnostic Reasoning based on Structure and Behavior”, Artificial Intelligence, 24, 347–410.
G. DeJong, R. Mooney (1986), “Explanation based learning: An alternative view”, Machine Learning, 1, 47–80.
G. DeJong (1990), “Plausible inference vs. abduction”, Proc. AAAI Symposium on Automated Abduction (Stanford, CA), pp. 48–51.
K. Eshghi, R. Kowalski (1989), “Abduction Compared with Negation by Failure”, Proc. 6th Int. Conf. on Logic Programming (Lisbon, Portugal), pp. 234–254.
H. Geffner (1990), “Causal theories for default and abductive reasoning”, Proc. AAAI Symposium on Automated Abduction (Stanford, CA), pp. 150–154.
M. Genesereth (1984), “The use of design descriptions in automated diagnosis”, Artificial Intelligence, 24, 411–436.
R. Hartley, M. Coombs (1990), “Abduction in model generative reasoning”, Proc. AAAI Symposium on Automated Abduction (Stanford, CA), pp. 130–134.
R. Keller (1988), “Defining operationality for EBL”, Artificial Intelligence, 35, 227–242.
J. de Kleer, J. Seely Brown (1986), “Theories of causal ordering”, Artificial Intelligence, 29, 33–61.
H. Levesque (1987), “A knowledge-level account of abduction”, Proc. IJCAI (Detroit, MI), pp. 1061–1067.
T. Mitchell, R. Keller, S. Kedar-Cabelli (1986), “Explanation based generalization”, Machine Learning, 1, 47–80.
S. Morris, P. O'Rorke (1990), “An approach to theory revision using abduction”, Proc. AAAI Symposium on Automated Abduction (Stanford, CA), pp. 33–37.
C. Peirce (1931–1958), Collected Papers of Charles Sanders Peirce, C. Hartchorne, P. Weiss and A. Burks (Eds.), Harvard University Press (Cambridge, MA).
D. Poole (1988), “Representing knowledge for logic-based diagnosis”, Proc. Int. Conf. on Fifth Generation Computer Systems (Tokyo, Japan), pp. 1282–1290.
R. Reiter (1984), “A theory of diagnosis from first principles”, Artificial Intelligence, 32, 57–95.
P. Torasso, L. Console (1989), Diagnostic problem solving, Van Nostrand Reinhold.
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© 1991 Springer-Verlag Berlin Heidelberg
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Botta, M., Brancadori, F., Ravotto, S., Saitta, L., Sperotto, S. (1991). Use of a causal model to learn diagnostic knowledge in a real domain. In: Ardizzone, E., Gaglio, S., Sorbello, F. (eds) Trends in Artificial Intelligence. AI*IA 1991. Lecture Notes in Computer Science, vol 549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54712-6_235
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DOI: https://doi.org/10.1007/3-540-54712-6_235
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