Abstract
APT system integrates Machine Learning (ML) and Knowledge Acquisition (KA) methods in the same framework. Both kinds of methods closely cooperate to concur in the same purpose: the acquisition, validation and maintenance of problem-solving knowledge. The methods are based on the same assumption: knowledge acquisition and learning are done through experimentation, classification and comparison of concrete cases. This paper details APT's mechanisms and shows through examples and applications how APT underlying principles allow various methods to fruitfully collaborate.
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References
Aussenac N., “Conception d'une méthodologie et d'un outil d'acquisition de connaissances expertes”. Thèse d'Informatique, Université Paul Sabatier de Toulouse, 1989.
Bareiss R., “Exemplar-Based Knowledge Acquisition”, Academic Press Inc, San Diego, 1989.
Bento C., Costa E., Fereira J.L., “APT as a knowledge elicitation tool in the domain of hypertension therapy “, MLT Esprit Project report, January 1991.
Bento C., Costa E., Fereira J.L., “APT as a knowledge acquisition tool in the domain of Analysis ”, MLT Esprit Project report, October 1991.
Bisson G.,“Learning in FOL with a similarity measure”, Proceedings of AAAI, eds AAAI press, (to appear), July 1992.
Carbonell J. G., Knoblock C. A., Minto N. S., “PRODIGY: An integraated Architecture for Planning and Learning,”, in Architectures for Intelligence, K. VanLehn (Ed), 1990. De Raedt L., “Interactive Concept-Learning”, Ph.D. diss. Katholieke Universiteit Leuven,1991.
Fikes, R. E. and Nilsson N. J., “STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2 pp. 198–208, 1971
Kodratoff Y., Addis T., Mantaras R.L, Morik K., Plaza E. “Four Stances on Knowledge Acquisition and Machine Learning”, Proceeding EWSL 91, Springer-Verlag, Porto, 1991.
Kodratoff Y. and Ganascia, J. G., “Improving the generalization step in Learning”, in Machine Learning II: An Artificial Intelligence Approach, RR.S. Michalski J.G. Carbonell and T.M. Mitchell (Eds) Morgan Kaufmann, 1986.
Lopez B., Meseguer P. and Plaza E., “Knowledge Based Systems Validation: A State of the Art”, in AICOM Vol. 3, June 1990.
Mitchell, T. M., “Version Spaces: An Approach to Concept Learning”, Ph.D. diss. Standford University, 1978.
Mitchell, T. M., Mahadevan, S., Steinberg, L. I. “LEAP: A Learning Apprentice for VLSI Design ”, in Machine Learning III: An Artificial Intelligence Approach, pp 271–289, Kodratoff Y. & Michalski R. (eds), Morgan Kaufmann, 1989.
Morik K. “Underlying Assumptions of Knowledge Acquisition and Machine Learning”, in Knowledge Acquisition, Vol. 3, No. 2, pp. 137–156, 1991.
Morik K. “Sloppy modeling”, in Knowldge Representation and Organisation in Machine Learning, Morik K. (ed), 1989.
Nedellec C. “A smallest generalization steps strategy” in the proceedings of the International Workshop on Machine Learning, IWML-91 Chicago, 1991
Nedellec C. “APT User's Guide”, MLT Esprit Project Deliverable 4.2, 1991.
Rouveirol C., “Semantic Model for induction of First Order Theories”, Proceeding of 12th LTCAL pp 685–690 Sydney, 1991.
Sammut, C.A. and Banerji, R.B., “Learning Concepts by asking Questions”, in Machine Learning II: An Artificial Intelligence Approach, pp 167–192, R.S. Michalski J.G. Carbonell and T.M. Mitchell (Eds) Morgan Kaufmann, 1986.
Shapiro, E. Y. “Algorithmic program debugging”, MIT press, Cambridge, MA, 1983.
Smith B. D. and Rosenbloom P. S., “Incremental Non-Backtracking Focusing: A Polynomially Bounded Generalization Algorithm for Version Space.”, in proceedings of AAAI-90.
Tecuci G. & Kodratoff Y., “Apprenticeship Learning in Nonhomogeneous Domain Theories”, in Machine Learning III: An Artificial Intelligence Approach, Kodratoff Y. & Michalski R. (eds), Morgan Kaufmann, 1989.
Wielienga B.J., Shreiber A. TH., Breuker J.A., “KADS: A Modelling Approach to Knowledge Engineering”, Submitted to Knowledge Acquisition, May 8 1991.
Wrobel S., “Demand-Driven Concept Formation”, in Knowldge Representation and Organisation in Machine Learning, Morik K. (ed), 1989.
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© 1992 Springer-Verlag Berlin Heidelberg
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Nedellec, C., Causse, K. (1992). Knowledge refinement using Knowledge Acquisition and Machine Learning Methods. In: Wetter, T., Althoff, KD., Boose, J., Gaines, B.R., Linster, M., Schmalhofer, F. (eds) Current Developments in Knowledge Acquisition — EKAW '92. EKAW 1992. Lecture Notes in Computer Science, vol 599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55546-3_40
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DOI: https://doi.org/10.1007/3-540-55546-3_40
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