Polynomial inference of universal automata from membership and equivalence queries

We present a MAT learning algorithm that infers the universal automaton for a regular target language using a polynomial number of queries with respect to that automaton. The universal automaton is one of the numerous canonical characterizations for regular languages. Our learner is based on the concept of an observation table, and we adapt the necessary notions and definitions from the literature to the case of universal automata.

• Type: Journal
• Year: 2016
• Where: IANDC
• Authors: Johanna Björklund, Henning Fernau, Anna Kasprzik
• Keywords: Biometrics, IANDC 2016