Residuality and Learning for Nondeterministic Nominal Automata

  title={Residuality and Learning for Nondeterministic Nominal Automata},
  author={Joshua Moerman and Matteo Sammartino},
  journal={Log. Methods Comput. Sci.},
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… 

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