TY - GEN

T1 - Learning regular omega languages

AU - Angluin, Dana

AU - Fisman, Dana

N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84910088781&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-11662-4_10

DO - 10.1007/978-3-319-11662-4_10

M3 - Conference contribution

AN - SCOPUS:84910088781

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 125

EP - 139

BT - Algorithmic Learning Theory - 25th International Conference, ALT 2014, Proceedings

A2 - Auer, Peter

A2 - Clark, Alexander

A2 - Zeugmann, Thomas

A2 - Zilles, Sandra

PB - Springer Verlag

T2 - 25th International Conference on Algorithmic Learning Theory, ALT 2014

Y2 - 8 October 2014 through 10 October 2014

ER -