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Incorporating hypothetical knowledge into the process of inductive synthesis

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Algorithmic Learning Theory (ALT 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1160))

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Abstract

The problem of inductive inference of functions from hypothetical knowledge is investigated in this paper. This type of inductive inference could be regarded as a generalization of synthesis from examples that can be directed not only by input/output examples but also by knowledge of, e. g., functional description's syntactic structure or assumptions about the process of function evaluation. We show that synthesis of this kind is possible by efficiently enumerating the hypothesis space and illustrate it with several examples.

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References

  1. P. Langley, G. Bradshaw, H.A. Simon. Rediscovering chemistry with the BACON system. In Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell, T.M. Mitchell (eds.), Tioga Press, Palo Alto, CA, 1983.

    Google Scholar 

  2. P. Langley, H.A. Simon, G. Bradshaw. Heuristics for Empirical Discovery. In Computational Models of Learning, L. Bolc (ed.), Springer-Verlag, 1987.

    Google Scholar 

  3. J.H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, 1992.

    Google Scholar 

  4. J.R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.

    Google Scholar 

  5. J.R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, 1994.

    Google Scholar 

  6. J.M. Barzdin and G.J. Barzdin. Rapid construction of algebraic axioms from samples. Theoretical Computer Science 90. 1991. pp. 199–208.

    Article  Google Scholar 

  7. J. Barzdins, G. Barzdins, K. Apsitis, U. Sarkans. Towards Efficient Inductive Synthesis of Expressions from input/output Examples. Lecture Notes in Artificial Intelligence, vol. 744.-1993. pp. 59–72.

    Google Scholar 

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, University of Latvia, Raiņa bulvāris 29, LV-1459, Riga, Latvia

    Jānis Bārzdiņš & Ugis Sarkans

Authors
  1. Jānis Bārzdiņš
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  2. Ugis Sarkans
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Editor information

Setsuo Arikawa Arun K. Sharma

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© 1996 Springer-Verlag Berlin Heidelberg

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Bārzdiņš, J., Sarkans, U. (1996). Incorporating hypothetical knowledge into the process of inductive synthesis. In: Arikawa, S., Sharma, A.K. (eds) Algorithmic Learning Theory. ALT 1996. Lecture Notes in Computer Science, vol 1160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61863-5_43

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  • DOI: https://doi.org/10.1007/3-540-61863-5_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61863-8

  • Online ISBN: 978-3-540-70719-6

  • eBook Packages: Springer Book Archive

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