Abstract
This paper summarizes recent research results on applications of computational learning theory to problems involving rich systems of knowledge representation, in particular, first-order logic and extensions thereof.
Research support was provided by the Office of Naval Research under contracts Nos. N00014-87-K-0401 and N00014-89-J-1725.
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References
Blumer, A., A. Ehrenfeucht, D. Haussler, & M. Warmuth (1987). Learnability and the Vapnik-Chervonenkis Dimension (Technical Report UCSC-CRL-87-20). Santa Cruz: University of California.
Case, J. & Fulk, M. (Eds.) (1990). Proceedings of the third annual workshop on computational learning theory, San Mateo, CA: Morgan-Kaufmann.
Chang, C. C. & Keisler, H. J. (1973). Model theory, Amsterdam: North-Holland.
Ebbinghaus, H.-D. (1985). Extended logics: the general framework. In Barwise, J. & S. Feferman (eds.), Model theoretic logics. New York: Springer-Verlag.
Gaifman, H., Osherson, D. & Weinstein, S. (1990). A reason for theoretical terms. Erkenntnis, 32, 149–159.
Glymour, C. (1985). Inductive inference in the limit. Erkenntnis, 22, 23–31.
Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.
Hempel, C. G. (1965) Aspects of scientific explanation and other essays in the philosophy of science. The Free Press.
Langley, P., Bradshaw, G., & Simon, H. (1983). Rediscovering chemistry with the BACON system. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.) Machine learning: An artificial intelligence approach. Palo Alto, CA: Tioga.
Langley, P. & Nordhausen, B. (1986). A framework for empirical discovery. In Proceedings of the International Meeting on Advances in Learning, Les Arcs, France.
Osherson, D., Stob, M., & Weinstein, S. (1986). Systems that Learn. Cambridge, MA: MIT Press.
Osherson, D., Stob, M., & Weinstein, S. (1989). On approximate truth. In R. Rivest, D. Haussler, & M. Warmuth (Eds.), Proceedings of the second annual workshop on computational learning theory. San Mateo, CA: Morgan-Kaufmann.
Osherson, D., Stob, M., & Weinstein, S. (1989). A theory of approximate truth. (Technical Report). Cambridge, MA: M.I.T.
Osherson, D. & Weinstein, S. (1986). Identification in the limit of first-order structures. Journal of Philosophical Logic, 15, 55–81.
Osherson, D., Stob, M., & Weinstein, S. (1988). Mechanical learners pay a price for Bayesianism. Journal of Symbolic Logic, 53, 1245–1251.
Osherson, D. & Weinstein, S. (1989). Paradigms of truth detection. Journal of Philosophical Logic, 18, 1–42.
Osherson, D., Stob, M., & Weinstein, S. (1991). A universal inductive inference machine,” Journal of Symbolic Logic, 56, 661–672.
Osherson, D., Stob, M., & Weinstein, S., (in press). A universal method of scientific inquiry. Machine Learning.
Osherson, D., Stob, M., & Weinstein, S. (1991). New directions in automated scientific discovery. Information Sciences.
Osherson, D. & Weinstein, S. (1989). Identifiable collections of countable structures. Philosophy of Science, 56, 95–105.
Osherson, D. & Weinstein, S. (1990). On advancing simple hypotheses. Philosophy of Science, 57, 266–277.
Osherson, D. & Weinstein, S. (in press). Relevant consequence and scientific discovery. Journal of Philosophical Logic.
Rivest, R., Haussler, D., & Warmuth, M. (Eds.) (1989). Proceedings of the second annual workshop on computational learning theory. San Mateo, CA: Morgan-Kaufmann.
Schurz, G. (1991). Relevant deduction. Erkenntnis.
Schurz, G. & Weingartner, P. (1987). Verisimilitude defined by relevant consequence-elements: A new reconstruction of Popper’s idea. In T. A. Kuipers (Ed.), What is closer-to-the-truth? Amsterdam: Rodopi.
Shapiro, E. (1981). An algorithm that infers theories from facts. In Proceedings of the seventh international joint conference on artificial intelligence.
Valiant, L. (1984). A theory of the learnable. Communications of the ACM, 27, 1134–1142.
Weingartner, P. (1988). Remarks on the consequence-class of theories. In E. Scheibe (Ed.), The role of experience in science. Walter de Gruyter.
Weingartner, P. & Schurz, G. (1986). Paradoxes solved by simple relevance criteria. Logique et Analyse.
J. Zytkow (1987). Combining many searches in the FAHRENHEIT discovery system. In Proceedings of the Fourth International Workshop on Machine Learning, Irvine CA.
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© 1993 Kluwer Academic Publishers
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Osherson, D., Weinstein, S. (1993). On the Automated Discovery of Scientific Theories. In: Meyrowitz, A.L., Chipman, S. (eds) Foundations of Knowledge Acquisition. The Springer International Series in Engineering and Computer Science, vol 195. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-27366-2_10
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DOI: https://doi.org/10.1007/978-0-585-27366-2_10
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