Abstract
Learning by discovery aims at bringing to light laws from a set of numerical or symbolic data. Our work deals with the improvement of the discovery system ABACUS created by Michalski and Falkenhainer, and in particular, with the way the system makes use of informative accuracy of the data. ABACUS, like most others current discovery systems does not use this information in the real physical sense, that means accuracy given by the measure device. However, in experimental domains accuracy cannot obviously be separated from the data. In this paper, we show how, when used in a more realistic manner, this information can significantly improve not only the accuracy of the results but also the efficiency of the search algorithm. Several additional modifications to ABACUS to improve the robustness of the system without losing generality will also be described.
Chapter PDF
References
Davenport J., Siret Y., Tournier E. Calcul formel. Systèmes et algorithmes de manipulations algebriques. Collection etudes et recherche en informatique, Eds Masson, Paris, 1986.
Eurin M., Guimiot H. Physique, Classiques HACHETTE, 1953.
Falkenhainer B.C., Michalski R.S. Integrating Quantitative and Qualitative Discovery: The ABACUS system. Machine Learning Journal, vol. 3, 1986.
Falkenhainer B.C., Michalski R.S. Integrating Quantitative and Qualitative Discovery. Machine Learning: An Artificial Intelligence Approach, vol III, R.S. Michalski, J.G. Carbonell, T.M. Mitchell (Eds.), 1990.
Greene G.H. The ABACUS.2 system for quantitative discovery: Using dependencies to discover non-linear terms, MLI 88-17 TR-11-88, 1988.
Joyal M. Cours de physique, Vol. 3 Electricite, Eds Masson & Cie, 1956.
Langley P., Bradshaw G.L., Simon H. BACON.5: the discovery of conservation laws. Proceedings of the seventh International Joint Conference on Artificial Intelligence, p 121–126, 1985.
Langley P., Zytkow J., Simon H.and Bradshaw G.L. The search for regularity: Four aspects of scientific discovery in Machine Learning: An Artificial Intelligence Approach, volume II, Michalski R.S., Carbonell J.G., Mitchell T.M.(Eds.), Tioga, Palo Alto, Calif., 1986.
Langley P., Zytkow J., Simon H. and Bradshaw G.L. Scientific discovery. Computational explorations of the creative process. MIT press, Cambridge, MA, 1987.
Nordhausen B., Langley P. A robust approach to Numeric Discovery", Proceedings of the seventh International Conference on Machine Learning, p 411–418, edited by B.W. Porter and R.J. Mooney, Morgan Kauffman Publishers, Austin, 1990.
Zytkow J. M. Combining many searches in the FAHRENHEIT discovery system. Proceedings of the fourth International Workshop on Machine Learning, p 281–287, Morgan Kauffman Publishers, Irvine, 1987.
Zytkow J. M., Zhu J and Hussam, A., Automated discovery in a chemistry laboratory. Proceedings of the AAAI-90, AAAI Press, p 889–894, 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1991 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moulet, M. (1991). Using accuracy in scientific discovery. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017008
Download citation
DOI: https://doi.org/10.1007/BFb0017008
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-53816-5
Online ISBN: 978-3-540-46308-5
eBook Packages: Springer Book Archive