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On the Automated Discovery of Scientific Theories

  • Daniel Osherson
  • Scott Weinstein
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 195)

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.

Keywords

Truth Detection Inductive Inference Discovery Problem Approximate Truth Computational Learning Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. [1]
    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.Google Scholar
  2. [2]
    Case, J. & Fulk, M. (Eds.) (1990). Proceedings of the third annual workshop on computational learning theory, San Mateo, CA: Morgan-Kaufmann.Google Scholar
  3. [3]
    Chang, C. C. & Keisler, H. J. (1973). Model theory, Amsterdam: North-Holland.MATHGoogle Scholar
  4. [4]
    Ebbinghaus, H.-D. (1985). Extended logics: the general framework. In Barwise, J. & S. Feferman (eds.), Model theoretic logics. New York: Springer-Verlag.Google Scholar
  5. [5]
    Gaifman, H., Osherson, D. & Weinstein, S. (1990). A reason for theoretical terms. Erkenntnis, 32, 149–159.CrossRefGoogle Scholar
  6. [6]
    Glymour, C. (1985). Inductive inference in the limit. Erkenntnis, 22, 23–31.CrossRefGoogle Scholar
  7. [7]
    Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.CrossRefGoogle Scholar
  8. [8]
    Hempel, C. G. (1965) Aspects of scientific explanation and other essays in the philosophy of science. The Free Press.Google Scholar
  9. [9]
    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.Google Scholar
  10. [10]
    Langley, P. & Nordhausen, B. (1986). A framework for empirical discovery. In Proceedings of the International Meeting on Advances in Learning, Les Arcs, France.Google Scholar
  11. [11]
    Osherson, D., Stob, M., & Weinstein, S. (1986). Systems that Learn. Cambridge, MA: MIT Press.Google Scholar
  12. [12]
    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.Google Scholar
  13. [13]
    Osherson, D., Stob, M., & Weinstein, S. (1989). A theory of approximate truth. (Technical Report). Cambridge, MA: M.I.T.Google Scholar
  14. [14]
    Osherson, D. & Weinstein, S. (1986). Identification in the limit of first-order structures. Journal of Philosophical Logic, 15, 55–81.CrossRefGoogle Scholar
  15. [15]
    Osherson, D., Stob, M., & Weinstein, S. (1988). Mechanical learners pay a price for Bayesianism. Journal of Symbolic Logic, 53, 1245–1251.MATHCrossRefGoogle Scholar
  16. [16]
    Osherson, D. & Weinstein, S. (1989). Paradigms of truth detection. Journal of Philosophical Logic, 18, 1–42.CrossRefGoogle Scholar
  17. [17]
    Osherson, D., Stob, M., & Weinstein, S. (1991). A universal inductive inference machine,” Journal of Symbolic Logic, 56, 661–672.MATHCrossRefGoogle Scholar
  18. [18]
    Osherson, D., Stob, M., & Weinstein, S., (in press). A universal method of scientific inquiry. Machine Learning.Google Scholar
  19. [19]
    Osherson, D., Stob, M., & Weinstein, S. (1991). New directions in automated scientific discovery. Information Sciences.Google Scholar
  20. [20]
    Osherson, D. & Weinstein, S. (1989). Identifiable collections of countable structures. Philosophy of Science, 56, 95–105.CrossRefGoogle Scholar
  21. [21]
    Osherson, D. & Weinstein, S. (1990). On advancing simple hypotheses. Philosophy of Science, 57, 266–277.CrossRefGoogle Scholar
  22. [22]
    Osherson, D. & Weinstein, S. (in press). Relevant consequence and scientific discovery. Journal of Philosophical Logic.Google Scholar
  23. [23]
    Rivest, R., Haussler, D., & Warmuth, M. (Eds.) (1989). Proceedings of the second annual workshop on computational learning theory. San Mateo, CA: Morgan-Kaufmann.Google Scholar
  24. [24]
    Schurz, G. (1991). Relevant deduction. Erkenntnis.Google Scholar
  25. [25]
    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.Google Scholar
  26. [26]
    Shapiro, E. (1981). An algorithm that infers theories from facts. In Proceedings of the seventh international joint conference on artificial intelligence.Google Scholar
  27. [27]
    Valiant, L. (1984). A theory of the learnable. Communications of the ACM, 27, 1134–1142.MATHCrossRefGoogle Scholar
  28. [28]
    Weingartner, P. (1988). Remarks on the consequence-class of theories. In E. Scheibe (Ed.), The role of experience in science. Walter de Gruyter.Google Scholar
  29. [29]
    Weingartner, P. & Schurz, G. (1986). Paradoxes solved by simple relevance criteria. Logique et Analyse.Google Scholar
  30. [30]
    J. Zytkow (1987). Combining many searches in the FAHRENHEIT discovery system. In Proceedings of the Fourth International Workshop on Machine Learning, Irvine CA.Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Daniel Osherson
    • 1
  • Scott Weinstein
    • 2
  1. 1.IDIAPMartignySwitzerland
  2. 2.University of PennsylvaniaUSA

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