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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 365–378Cite as

Language learning under various types of constraint combinations

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

Abstract

Learnability of families of recursive languages from positive data is studied in the Gold paradigm of inductive inference. A large amount of work has focused on trying to understand how language learning ability of a learner is affected when it is constrained in various ways. For example, motivated by work in inductive logic, different notions of monotonicity have been studied which variously reflect the requirement that the learner's guess must monotonically ‘improve’ with regard to the target language. Various types of combinations of constraints such as monotonicity are defined and their relationships explored. Under one version of a disjunctive combination of a set of constraints, learning is considered successful as long as on any presentation of a language at least one of the constraints in the set is satisfied. It is also shown that a conjunctive combination of certain monotonicity constraints is less powerful than the set-theoretic intersection of the classes corresponding to the individual constraints.

The author would like to thank the anonymous reviewers for some useful suggestions

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Authors and Affiliations

  1. Department of Computer Science, James Cook University of North Queensland, QLD 4811, Townsville, Australia

    Shyam Kapur

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  1. Shyam Kapur
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Setsuo Arikawa Klaus P. Jantke

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© 1994 Springer-Verlag Berlin Heidelberg

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Kapur, S. (1994). Language learning under various types of constraint combinations. 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_77

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  • DOI: https://doi.org/10.1007/3-540-58520-6_77

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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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