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International Workshop on Analogical and Inductive Inference

International Workshop on Algorithmic Learning Theory

AII 1994, ALT 1994: Algorithmic Learning Theory pp 349–364Cite as

On monotonic strategies for learning r.e. languages

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 872))

Abstract

Overgeneralization is a major issue in identification of grammars for formal languages from positive data. Different formulations of monotonic strategies have been proposed to address this problem and recently there has been a flurry of activity investigating such strategies in the context of indexed families of recursive languages.

The present paper studies the power of these strategies to learn recursively enumerable languages from positive data. In particular, the power of strong monotonic, monotonic, and weak monotonic (together with their dual notions modeling specialization) strategies are investigated for identification of r.e. languages. These investigations turn out to be different from the previous investigations on learning indexed families of recursive languages and at times require new proof techniques.

A complete picture is provided for the relative power of each of the strategies considered. An interesting consequence is that the power of weak monotonic strategies is equivalent to that of conservative strategies. This result parallels the scenario for indexed classes of recursive languages. It is also shown that any identifiable collection of r.e. languages can also be identified by a strategy that exhibits the dual of weak monotonic property.

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Author information

Authors and Affiliations

  1. Department of Information Systems and Computer Science, National University of Singapore, Lower Kent Ridge Road, 0511, Singapore, Republic of Singapore

    Sanjay Jain

  2. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Arun Sharma

Authors
  1. Sanjay Jain
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  2. Arun Sharma
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Editor information

Setsuo Arikawa Klaus P. Jantke

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© 1994 Springer-Verlag Berlin Heidelberg

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Jain, S., Sharma, A. (1994). On monotonic strategies for learning r.e. languages. In: Arikawa, S., Jantke, K.P. (eds) Algorithmic Learning Theory. AII ALT 1994 1994. Lecture Notes in Computer Science, vol 872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58520-6_76

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  • DOI: https://doi.org/10.1007/3-540-58520-6_76

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58520-6

  • Online ISBN: 978-3-540-49030-2

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