Polynomial inference of universal automata from membership and equivalence queries
A MAT learning algorithm is presented that infers the universal automaton (UA) for a regular target language, using a polynomial number of queries with respect to that automaton. The UA is one of several canonical characterizations for regular languages. Our learner is based on the concept of an observation table, which seems to be particularly fitting for this computational model, and the necessary definitions are adapted from the literature to the case of UA.
observation tables query learning universal finite automata