@inproceedings{9dd82930c3e24b31af5e09997fe87759,
title = "Inferring Symbolic Automata",
abstract = "We study the learnability of symbolic finite state automata, a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results are positive. We provide a necessary condition for efficient learnability of SFAs in this paradigm, from which we obtain the first negative result. The main focus of our work lies in the learnability of SFAs under the paradigm of identification in the limit using polynomial time and data. We provide a necessary condition and a sufficient condition for efficient learnability of SFAs in this paradigm, from which we derive a positive and a negative result.",
keywords = "Characteristic sets, Query learning, Symbolic finite state automata",
author = "Dana Fisman and Hadar Frenkel and Sandra Zilles",
note = "Funding Information: Funding Dana Fisman: This research was partially supported by the Israel Science Foundation (ISF) grant 2507/21. Publisher Copyright: {\textcopyright} Dana Fisman, Hadar Frenkel, and Sandra Zilles.; 30th EACSL Annual Conference on Computer Science Logic, CSL 2022 ; Conference date: 14-02-2022 Through 19-02-2022",
year = "2022",
month = jan,
day = "27",
doi = "10.4230/LIPIcs.CSL.2022.21",
language = "English",
series = "Leibniz International Proceedings in Informatics, LIPIcs",
publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
pages = "21:1----21:19",
editor = "Florin Manea and Alex Simpson",
booktitle = "30th EACSL Annual Conference on Computer Science Logic, CSL 2022",
address = "Germany",
}