Extending AALpy with Passive Learning: A Generalized State-Merging Approach

TL;DR

This paper extends the AALpy automata learning library by adding a generalized, configurable implementation of passive state-merging algorithms within the red-blue framework, simplifying development of new algorithms.

Contribution

It introduces a flexible, unified implementation of passive state-merging algorithms in AALpy, facilitating easier development and experimentation.

Findings

Simplifies implementation of state-merging algorithms

Enables defining algorithms with minimal code

Supports various automaton types within a common framework

Abstract

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.