@inproceedings{26ce79f8f94648198c07e81a6851e23b,
title = "Learning regular languages via alternating automata",
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, NL∗outperforms 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 L∗NL∗UL∗ and AL∗.",
author = "Dana Angluin and Sarah Eisenstat and Dana Fisman",
year = "2015",
month = jan,
day = "1",
language = "American English",
series = "IJCAI International Joint Conference on Artificial Intelligence",
pages = "3308--3314",
editor = "Michael Wooldridge and Qiang Yang",
booktitle = "IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence",
note = "24th International Joint Conference on Artificial Intelligence, IJCAI 2015 ; Conference date: 25-07-2015 Through 31-07-2015",
}