On learning systolic languages

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We study the learning problem of systolic languages from queries and counterexamples. A systolic language is specified by a systolic automaton which is a kind of network consisting of uniformly connected processors(finite automata). In this article, we show that the class of binary systolic tree languages is learnable in polynomial time from the learning protocol what is called minimally adequate teacher. Since the class of binary systolic tree languages properly contains the class of regular languages, the main result in this paper gives a generalization of the corresponding Angluin’s result for regular languages.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 3rd Workshop, ALT 1992, Proceedings
EditorsKoichi Furukawa, Shuji Doshita, Klaus P. Jantke, Toyaki Nishida
PublisherSpringer Verlag
Pages41-52
Number of pages12
ISBN (Print)9783540573692
Publication statusPublished - 1993 Jan 1
Event3rd Workshop on Algorithmic Learning Theory, ALT 1992 - Tokyo, Japan
Duration: 1992 Oct 201992 Oct 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume743 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd Workshop on Algorithmic Learning Theory, ALT 1992
CountryJapan
CityTokyo
Period92/10/2092/10/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'On learning systolic languages'. Together they form a unique fingerprint.

  • Cite this

    Yokomori, T. (1993). On learning systolic languages. In K. Furukawa, S. Doshita, K. P. Jantke, & T. Nishida (Eds.), Algorithmic Learning Theory - 3rd Workshop, ALT 1992, Proceedings (pp. 41-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 743 LNAI). Springer Verlag.