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Grammatical Inference and Learning

  • Chapter
Formal Languages and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 148))

Abstract

Grammatical inference is known as one of the most attractive paradigms of scientific learning that is nowadays a classical but still novel discipline. The problem of grammatical inference is roughly to infer (discover) a grammar that generates a given set of sample sentences in some manner that is supposed to be realized by some algorithmic device, usually called inference algorithm. Therefore, grammatical inference can be taken as one of the typical formulations for a broader word “learning”, and provides a good theoretical framework for investigating a learning process. The goal of this chapter is to present standard but important results in the area of grammatical inference as learning, including not only theoretical fruits but also interesting applications of learning algorithms.

The author is deeply indebted to Satoshi Kobaashi for his valuable comments on the draft of this article.

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  1. Department of Mathematics, School of Education, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, 169-8050, Tokyo, Japan

    Takashi Yokomori

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  1. Research Group on Mathematical Lingustics, Rovira i Virgili University, Pl. Imperial Tàrraco 1, 43005, Tarragona, Spain

    Carlos Martín-Vide

  2. Faculty of Mathematics, University of Bucharest, Str. Academiei 14, 70109, Bucharest, Romania

    Victor Mitrana

  3. Institute of Mathematics, Romanian Academy, PO Box 1-764, 70700, Bucharest, Romania

    Gheorghe Păun

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Yokomori, T. (2004). Grammatical Inference and Learning. In: Martín-Vide, C., Mitrana, V., Păun, G. (eds) Formal Languages and Applications. Studies in Fuzziness and Soft Computing, vol 148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39886-8_27

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