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Active Learning of Mealy Machines with Timers
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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.
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Cited by 2 Pith papers
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Automata Learning -- Expect Delays!
A two-stage active learning method for Mealy machines with stochastic transition delays uses learned structure to plan efficient delay sampling, outperforming naive sampling-based L*.
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Learning Event-recording Automata Passively
LEAP is a state-merging algorithm that passively learns event-recording automata from positive and negative symbolic timed words, with an NP-completeness result for the merge check and a completeness proof via charact...
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