Active Learning of Mealy Machines with Timers
Active Learning of Mealy Machines with Timers
We present the first algorithm for query learning Mealy machines with timers in a black-box context. Our algorithm is an extension of the L# algorithm of Vaandrager et al. to a timed setting. We rely on symbolic queries which empower us to reason on untimed executions while learning. Similarly to the algorithm for learning timed automata of Waga, these symbolic queries can be realized using finitely many concrete queries. Experiments with a prototype implementation show that our algorithm is able to efficiently learn realistic benchmarks.
57 pages, 13 figures. Published at QEST+FORMATS 2025
- University of Rennes 1 France
- University of Antwerp Belgium
- Radboud University Nijmegen Netherlands
- University of Southern Brittany France
- Radboud Universiteit Nijmegen
Machine Learning, FOS: Computer and information sciences, model learning, Timed systems, Formal Languages and Automata Theory (cs.FL), active automata learning, F.4.3, 68Q45, [INFO] Computer Science [cs], Formal Languages and Automata Theory, Machine Learning (cs.LG)
Machine Learning, FOS: Computer and information sciences, model learning, Timed systems, Formal Languages and Automata Theory (cs.FL), active automata learning, F.4.3, 68Q45, [INFO] Computer Science [cs], Formal Languages and Automata Theory, Machine Learning (cs.LG)
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