Learning regular omega languages

Dana Angluin, Dana Fisman

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

19 Scopus citations


We provide an algorithm for learning an unknown regular set of infinite words, using membership and equivalence queries. Three variations of the algorithm learn three different canonical representations of omega regular languages, using the notion of families of dfas. One is of size similar to L$, a dfa representation recently learned using L∗ [7]. The second is based on the syntactic forc, introduced in [14]. The third is introduced herein. We show that the second can be exponentially smaller than the first, and the third is at most as large as the first two, with up to a quadratic saving with respect to the second.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 25th International Conference, ALT 2014, Proceedings
EditorsPeter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
PublisherSpringer Verlag
Number of pages15
ISBN (Electronic)9783319116617
StatePublished - 1 Jan 2014
Externally publishedYes
Event25th International Conference on Algorithmic Learning Theory, ALT 2014 - Bled, Slovenia
Duration: 8 Oct 201410 Oct 2014

Publication series

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


Conference25th International Conference on Algorithmic Learning Theory, ALT 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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