Inferring Symbolic Automata

Dana Fisman, Hadar Frenkel, Sandra Zilles

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

26 Downloads (Pure)

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.

Original languageEnglish
Title of host publication30th EACSL Annual Conference on Computer Science Logic, CSL 2022
EditorsFlorin Manea, Alex Simpson
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959772181
DOIs
StatePublished - 1 Feb 2022
Event30th EACSL Annual Conference on Computer Science Logic, CSL 2022 - Virtual, Gottingen, Germany
Duration: 14 Feb 202219 Feb 2022

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume216
ISSN (Print)1868-8969

Conference

Conference30th EACSL Annual Conference on Computer Science Logic, CSL 2022
Country/TerritoryGermany
CityVirtual, Gottingen
Period14/02/2219/02/22

Keywords

  • Characteristic sets
  • Query learning
  • Symbolic finite state automata

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Inferring Symbolic Automata'. Together they form a unique fingerprint.

Cite this