Paper on Compositional Automata Learning accepted at FASE’23

The paper “Compositional Automata Learning of Synchronous Systems” by Thomas Neele and Matteo Sammartino was accepted for presentation at FASE’23, which will take place in Paris, France. This paper proposes an extension for the classic L* automata learning algorithm, so that systems consisting of multiple communicating automata can be learning in a compositional fashion. This means far fewer membership and/or equivalence queries are required to learn the behaviour of such systems.