Query learning algorithm for residual symbolic finite automata

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


We propose a query learning algorithm for residual symbolic finite automata (RSFAs). Symbolic finite automata (SFAs) are finite automata whose transitions are labeled by predicates over a Boolean algebra, in which a big collection of characters leading the same transition may be represented by a single predicate. Residual finite automata (RFAs) are a special type of non-deterministic finite automata which can be exponentially smaller than the minimum deterministic finite automata and have a favorable property for learning algorithms. RSFAs have both properties of SFAs and RFAs and can have more succinct representation of transitions and fewer states than RFAs and deterministic SFAs accepting the same language. The implementation of our algorithm efficiently learns RSFAs over a huge alphabet and outperforms an existing learning algorithm for deterministic SFAs. The result also shows that the benefit of non-determinism in efficiency is even larger in learning SFAs than non-symbolic automata.

Original languageEnglish
Pages (from-to)140-153
Number of pages14
JournalElectronic Proceedings in Theoretical Computer Science, EPTCS
Publication statusPublished - 2019 Sept 18
Event10th International Symposium on Games, Automata, Logics, and Formal Verification, G and ALF 2019 - Bordeaux, France
Duration: 2019 Sept 22019 Sept 3


Dive into the research topics of 'Query learning algorithm for residual symbolic finite automata'. Together they form a unique fingerprint.

Cite this