Residuality and Learning for Nondeterministic Nominal Automata
Abstract
We are motivated by the following question: which data languages admit an active learning algorithm? This question was left open in previous work by the authors, and is particularly challenging for languages recognised by nondeterministic automata. To answer it, we develop the theory of residual nominal automata, a subclass of nondeterministic nominal automata. We prove that this class has canonical representatives, which can always be constructed via a finite number of observations. This property enables active learning algorithms, and makes up for the fact that residuality  a semantic property  is undecidable for nominal automata. Our construction for canonical residual automata is based on a machineindependent characterisation of residual languages, for which we develop new results in nominal lattice theory. Studying residuality in the context of nominal languages is a step towards a better understanding of learnability of automata with some sort of nondeterminism.
 Publication:

arXiv eprints
 Pub Date:
 October 2019
 arXiv:
 arXiv:1910.11666
 Bibcode:
 2019arXiv191011666M
 Keywords:

 Computer Science  Formal Languages and Automata Theory;
 F.4.3
 EPrint:
 Logical Methods in Computer Science, Volume 18, Issue 1 (February 3, 2022) lmcs:9038