Residuality and Learning for Nondeterministic Nominal Automata

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