Skip to main content
SpringerLink
Log in

Inductive inference with bounded mind changes

  • Technical Papers
  • Conference paper
  • First Online:
Algorithmic Learning Theory (ALT 1992)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 743))

Included in the following conference series:

Abstract

In this paper, we deal with inductive inference for a class of recursive languages with a bounded number of mind changes. We introduce an n-bounded finite tell-tale and a pair of n-bounded finite tell-tales of a language, and present a necessary and sufficient condition for a class to be inferable with bounded mind changes, when the equivalence of any two languages in the class is effectively decidable. We also show that the inferability of a class from positive data strictly increases, when the allowed number of mind changes increases. In his previous paper, Mukouchi gave necessary and sufficient conditions for a class of recursive languages to be finitely identifiable, that is, to be inferable without any mind changes from positive or complete data. The results we present in this paper are natural extensions of the above results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Angluin, D., Inductive inference of formal languages from positive data, Information and Control 45 (1980), 117–135.

    Article  Google Scholar 

  2. Angluin, D., Finding patterns common to a set of strings, Proc. 11th Annual Symposium on Theory of Computing (1979), 130–141.

    Google Scholar 

  3. Case, J. and Lynes, C., Machine inductive inference and language identification, Proc. of the International Colloquium on Automata, Languages and Programming(ICALP) (1982), 107–115.

    Google Scholar 

  4. Case, J. and Smith, C., Comparison of identification criteria for machine inductive inference, Theoretical Computer Science 25 (1983), 193–220.

    Article  Google Scholar 

  5. Gold, E.M., Language identification in the limit, Information and Control 10 (1967), 447–474.

    Article  Google Scholar 

  6. Sato, M. and Umayahara, K., Inductive inferability for formal languages from positive data, Proc. 2nd Workshop on Algorithmic Learning Theory (1991), 84–92.

    Google Scholar 

  7. Shinohara, T., Inductive inference from positive data is powerful, Proc. 3rd Workshop on Comput. Learning Theory (1990), 97–110.

    Google Scholar 

  8. Shinohara, T., Inductive inference of monotonic formal systems from positive data, Proc. 1st Workshop on Algorithmic Learning Theory (1990), 339–351.

    Google Scholar 

  9. Mukouchi, Y., Characterization of pattern languages, Proc. 2nd Workshop on Algorithmic Learning Theory (1991), 93–104.

    Google Scholar 

  10. Mukouchi, Y., Characterization of finite identification, to appear in Proc. 3rd International Workshop on Analogical and Inductive Inference (1992).

    Google Scholar 

  11. Wright, K., Identification of unions of languages drawn from an identifiable class, Proc. 2nd Workshop on Comput. Learning Theory (1989), 328–333.

    Google Scholar 

Download references

Author information

Author notes

  1. Yasuhito Mukouchi

    Present address: Research Institute of Fundamental Information Science, Kyushu University 33, 812, Fukuoka, Japan

Authors and Affiliations

  1. Department of Information Systems, Kyushu University 39, 816, Kasuga, Japan

    Yasuhito Mukouchi

Authors
  1. Yasuhito Mukouchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasuhito Mukouchi .

Editor information

Shuji Doshita Koichi Furukawa Klaus P. Jantke Toyaki Nishida

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mukouchi, Y. (1993). Inductive inference with bounded mind changes. In: Doshita, S., Furukawa, K., Jantke, K.P., Nishida, T. (eds) Algorithmic Learning Theory. ALT 1992. Lecture Notes in Computer Science, vol 743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57369-0_33

Download citation

  • DOI: https://doi.org/10.1007/3-540-57369-0_33

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57369-2

  • Online ISBN: 978-3-540-48093-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation