2024-03-28T10:50:10Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/872902022-11-24T17:05:23Zhdl_2115_20053hdl_2115_145On the amount of nonconstructivity in learning formal languages from textJain, SanjayStephan, FrankZeugmann, ThomasInductive inferenceLearning in the limitNon-constructivityFormal languages007Nonconstructive 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.ElsevierJournal Articleapplication/pdfhttp://hdl.handle.net/2115/87290https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/87290/1/noncons.pdf0890-54011090-2651AA1067013XInformation and computation2811046682020-11enginfo:doi/10.1016/j.ic.2020.104668©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/author