On the amount of nonconstructivity in learning formal languages from text

Jain, Sanjay; Stephan, Frank; Zeugmann, Thomas

doi:10.1016/j.ic.2020.104668


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On the amount of nonconstructivity in learning formal languages from text

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Title:	On the amount of nonconstructivity in learning formal languages from text
Authors:	Jain, Sanjay Browse this author
	Stephan, Frank Browse this author
	Zeugmann, Thomas Browse this author
Keywords:	Inductive inference
	Learning in the limit
	Non-constructivity
	Formal languages
Issue Date:	Nov-2020
Publisher:	Elsevier
Journal Title:	Information and computation
Volume:	281
Start Page:	104668
Publisher DOI:	10.1016/j.ic.2020.104668
Abstract:	Nonconstructive computations by various types of machines and automata have been considered by, for example, Karp and Lipton as well as Freivalds. They allow to regard more complicated algorithms from the viewpoint of much more primitive computational devices. The amount of nonconstructivity is a quantitative characterization of the distance between types of computational devices with respect to solving a specific problem. This paper studies the amount of nonconstructivity needed to learn classes of formal languages. Different learning types are compared with respect to the amount of nonconstructivity needed to learn indexable classes and recursively enumerable classes, respectively, of formal languages from positive data. Matching upper and lower bounds for the amount of nonconstructivity needed are shown. (C) 2020 Elsevier Inc. All rights reserved.
Rights:	©2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Rights:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Type:	article (author version)
URI:	http://hdl.handle.net/2115/87290
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: Zeugmann Thomas

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