An Active Automata Learning Library Written in Python
AALpy
AALpy is a light-weight active automata learning library written in pure Python. By implementing a single method and a few lines of configuration, you can start learning automata.
Whether you work with regular languages or you would like to learn models of reactive systems, AALpy supports a wide range of modeling formalisms, including deterministic, non-deterministic, and stochastic automata. You can use it to learn deterministic finite automata, Moore machines, and Mealy machines of deterministic systems. If the system that you would like to learn shows non-deterministic or stochastic behavior, AALpy allows you to learn observable nondeterministic finite-state machines, Markov decision processes, or stochastic transducers.
AALpy enables efficient learning by providing a large array of equivalence oracles, implementing various conformance testing strategies. Learning is mostly based on Angluin’s L*