Polynomial Identification of ω-Automata

Dana Angluin, Dana Fisman, Yaara Shoval

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We study identification in the limit using polynomial time and data for models of ω-automata. On the negative side we show that non-deterministic ω-automata (of types Büchi, coBüchi, Parity or Muller) can not be polynomially learned in the limit. On the positive side we show that the ω-language classes IB, IC, IP, and IM that are defined by deterministic Büchi, coBüchi, parity, and Muller acceptors that are isomorphic to their right-congruence automata (that is, the right congruences of languages in these classes are fully informative) are identifiable in the limit using polynomial time and data. We further show that for these classes a characteristic sample can be constructed in polynomial time.

Original languageEnglish
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems- 26th International Conference, TACAS 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Proceedings, Part II
EditorsArmin Biere, David Parker
PublisherSpringer
Pages325-343
Number of pages19
ISBN (Print)9783030452360
DOIs
StatePublished - 1 Jan 2020
Event26th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020 - Dublin, Ireland
Duration: 25 Apr 202030 Apr 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12079 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2020, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020
Country/TerritoryIreland
CityDublin
Period25/04/2030/04/20

Keywords

  • characteristic sample
  • identification in the limit
  • ω-regular

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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