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International Workshop on Algorithmic Learning Theory

ALT 1993: Algorithmic Learning Theory pp 209–222Cite as

How to invent characterizable inference methods for regular languages

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 744))

Abstract

We present a general framework for constructing characterizable inference algorithms for regular languages. This general approach is based on the fact, that certain families of non-trivial and infinite regular languages can be described with finite tuples of finite sets of strings (or trees). It follows from this property, that the methods are able to inductively learn in the limit from positive samples only. It is also shown that if the mapping from these presentations to languages is monotonic, and can be approximated in an acceptable way, then this approximation can be used to construct the presentation of the smallest language in the family containing a given sample. We show the applicability of this framework for both the familiar regular string languages and the regular tree languages.

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Authors and Affiliations

  1. Department of Computer Science, University of Turku, Lemminkäisenkatu 14 A, SF-20520, Turku, Finland

    Timo Knuutila

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  1. Timo Knuutila
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Editor information

Klaus P. Jantke Shigenobu Kobayashi Etsuji Tomita Takashi Yokomori

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© 1993 Springer-Verlag Berlin Heidelberg

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Knuutila, T. (1993). How to invent characterizable inference methods for regular languages. In: Jantke, K.P., Kobayashi, S., Tomita, E., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1993. Lecture Notes in Computer Science, vol 744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57370-4_49

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  • DOI: https://doi.org/10.1007/3-540-57370-4_49

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57370-8

  • Online ISBN: 978-3-540-48096-9

  • eBook Packages: Springer Book Archive

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