Extending AALpy with Passive Learning: A Generalized State-Merging Approach
This work provides a tool for researchers and practitioners in automata learning to more easily implement and experiment with state-merging algorithms, but it is incremental as it builds on an existing library without introducing new fundamental methods.
The authors extended the AALpy automata learning library by adding a generalized implementation of state-merging in the red-blue framework for passive learning, which reduces implementation effort for such algorithms to defining compatibility criteria and scoring, enabling existing algorithms to be defined in just a few lines of code.
AALpy is a well-established open-source automata learning library written in Python with a focus on active learning of systems with IO behavior. It provides a wide range of state-of-the-art algorithms for different automaton types ranging from fully deterministic to probabilistic automata. In this work, we present the recent addition of a generalized implementation of an important method from the domain of passive automata learning: state-merging in the red-blue framework. Using a common internal representation for different automaton types allows for a general and highly configurable implementation of the red-blue framework. We describe how to define and execute state-merging algorithms using AALpy, which reduces the implementation effort for state-merging algorithms mainly to the definition of compatibility criteria and scoring. This aids the implementation of both existing and novel algorithms. In particular, defining some existing state-merging algorithms from the literature with AALpy only takes a few lines of code.