Abstract
In this paper, an alternative approach to the synthesis of Boolean functions is presented. Such an approach can be useful in the field of concept learning as well, provided that the semantics of non-specified instances is changed accordingly (i.e., from a don't-care to an unknown semantics). The underlying framework relies on the concept of exception, an exception being, for example, a 0 grouped together with 1's while performing the synthesis. It is shown that an exceptions-based synthesis can be adopted as a core mechanism to perform concept learning in an n-dimensional Boolean space. A learning system is sketched where the decision of re-calculating classification rules can be arbitrarily delayed, as new examples, not consistent with the current hypothesis, can be integrated within the system by temporarily storing them as exceptions.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Akers, S.B., “Binary Decision Diagrams,” IEEE Trans. on Computers, 27 (6), pp. 509–516, Jun 1978.
Bryant, R.E., “Graph-Based Algorithms for Boolean Function Manipulation,” IEEE Trans. on Computers, 35 (8), pp. 677–691, Aug 1986.
Michalski, R.S., “On the quasi-minimal solution of the general covering problem,” Proc. 5th Int. Symposium on Information Processing, Bled, Yugoslavia, pp. 125–128, 1969.
Quinlan, J.R., “Discovering rules from large collections of examples: a case study,” in Michie, D. (Ed.) “Expert systems in the microelectronic age,” Edinburgh University Press, 1979.
Rivest, R., “Learning Decision Lists,” Machine Learning, 2, pp. 229–246, 1987.
Carbonell, J., Michalski, R.S., and Mitchell, T.M., “An overview of machine learning,” in Machine Learning: An Artificial Intelligence Approach, pp. 3–24, Morgan Kaufmann, 1983.
Brayton, R.K., Hachtel, G.D., McMullen C.T., and Sangiovanni-Vincentelli, A.L., “Logic Minimization Algorithms for VLSI Synthesis,” Kluwer, 1984.
Reiter, R., “A logic for default reasoning,” Artificial Intelligence, 13, pp. 81–132, 1980.
Sandewall, E., “An Approach to the Frame Problem and Its Implementation,” in Machine Intelligence, 7, B. Meltzer and D. Michie (eds.), Edinburgh Univ. Press, Edinburgh, 1972.
Pagallo, G., and Haussler, D., “Boolean Feature Discovery in Empirical Learning,” Machine Learning, 5, pp. 71–79, 1990.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Armano, G. (1995). Exceptions-based synthesis of Boolean functions as a core mechanism to perform concept learning. In: Gori, M., Soda, G. (eds) Topics in Artificial Intelligence. AI*IA 1995. Lecture Notes in Computer Science, vol 992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60437-5_39
Download citation
DOI: https://doi.org/10.1007/3-540-60437-5_39
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60437-2
Online ISBN: 978-3-540-47468-5
eBook Packages: Springer Book Archive