A Detailed Account of Compositional Automata Learning through Alphabet Refinement

ArXi:2504.16624v3 Announce Type: replace Active automata learning infers automaton models of systems from behavioral observations, a technique successfully applied to a wide range of domains. Compositional approaches have recently emerged to address scalability to concurrent systems. We take a significant step beyond available results, including those by the authors, and develop a general technique for compositional learning of a synchronizing parallel system with an unknown decomposition.