Learning Automata with Side-Effects

Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability and hence optimizations that enable learning of compact representations are important. In this paper we develop a class of optimizations and an accompanying correctness proof for learning algorithms, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. Our approach allows us to capture known algorithms, develop new ones, and add optimizations. We provide a prototype implementation and experimental results.