Learning regular languages via alternating automata

Dana Angluin, Sarah Eisenstat, Dana Fisman

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

28 Scopus citations

Abstract

Nearly all algorithms for learning an unknown regular language, in particular the popular L algorithm, yield deterministic finite automata. It was recently shown that the ideas of L can be extended to yield non-deterministic automata, and that the respective learning algorithm, NLoutperforms L on randomly generated regular expressions. We conjectured that this is due to the existential nature of regular expressions, and NL might not outperform L on languages with a universal nature. In this paper we introduce UL - a learning algorithm for universal automata (the dual of non-deterministic automata); and AL - a learning algorithm for alternating automata (which generalize both universal and non-deterministic automata). Our empirical results illustrate the advantages and trade-offs among LNLUL and AL.

Original languageEnglish
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3308-3314
Number of pages7
ISBN (Electronic)9781577357384
StatePublished - 1 Jan 2015
Externally publishedYes
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January
ISSN (Print)1045-0823

Conference

Conference24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Country/TerritoryArgentina
CityBuenos Aires
Period25/07/1531/07/15

ASJC Scopus subject areas

  • Artificial Intelligence

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