Learning regular omega languages

Dana Angluin, Dana Fisman

Research output: Contribution to journalArticlepeer-review


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 regular omega languages using the notion of families of DFAs. One is of size similar to L$, a DFA representation recently learned using L by Farzan et al. The second is based on the syntactic FORC, introduced by Maler and Staiger. The third is introduced herein. We show that the second and third can be exponentially smaller than the first, and the third is at most as large as the second, with up to a quadratic saving with respect to the second.

Original languageAmerican English
Pages (from-to)57-72
Number of pages16
JournalTheoretical Computer Science
StatePublished - 18 Oct 2016
Externally publishedYes


  • Active learning
  • Büchi automata
  • Equivalence queries
  • Infinitary languages
  • Language inference
  • L⁎
  • Membership queries

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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