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Monotonic language learning

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Algorithmic Learning Theory (ALT 1992)

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

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Abstract

Learnability of families of recursive languages from positive data is studied in the Gold paradigm of inductive inference, where the learner obeys certain constraints motivated by work in inductive reasoning. Previously, various notions of monotonicity have been defined in the context of language learning. These constraints require that the learner's guess monotonically ‘improves’ with regard to the target language. In this paper, the ideas from inductive reasoning are instantiated in alternative ways. Links are established between the various new constraints both among themselves as well as with other well-known constraints, such as conservativeness. Exactly learnable families are characterized for prudent learners which obey various combinations of these constraints. Applications of these characterizations are also shown.

This work was supported in part by ARO grant DAAL 03-89-C-0031, DARPA grant N00014-90-J-1863, NSF grant IRI 90-16592 and Ben Franklin grant 91S.3078C-1.

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Authors and Affiliations

  1. Institute for Research in Cognitive Science, University of Pennsylvania, 3401 Walnut Street Rm 412C, 19104, Philadelphia, PA, USA

    Shyam Kapur

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  1. Shyam Kapur
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Shuji Doshita Koichi Furukawa Klaus P. Jantke Toyaki Nishida

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© 1993 Springer-Verlag Berlin Heidelberg

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Kapur, S. (1993). Monotonic language learning. 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_35

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  • DOI: https://doi.org/10.1007/3-540-57369-0_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57369-2

  • Online ISBN: 978-3-540-48093-8

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