Automata learning is a popular technique for inferring minimal
automata through membership and equivalence queries. We generalise
learning from automata to a large class of state-based systems, using
the theory of coalgebras. The approach relies on the use of logical
formulas as tests, based on a dual adjunction between states and
logical theories. This allows us to learn, e.g., labelled transition
Joint work with Clemens Kupke and Simone Barlocco.