IdMAT and LIndA enable learning any regular language consistent with an incomplete inductive teacher by reducing uncertainties to incremental SAT solving for tasks like language separation and invariant synthesis.
In: Bernardo, M., Issarny, V
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Introduces an L*-based active learning algorithm for deterministic MDPs that uses trace sampling to infer complete models including states and outperforms passive methods on accuracy with equal data.
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Automata Learning with an Incomplete but Inductive Teacher (Technical Report)
IdMAT and LIndA enable learning any regular language consistent with an incomplete inductive teacher by reducing uncertainties to incremental SAT solving for tasks like language separation and invariant synthesis.
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L*-Based Learning of Markov Decision Processes (Extended Version)
Introduces an L*-based active learning algorithm for deterministic MDPs that uses trace sampling to infer complete models including states and outperforms passive methods on accuracy with equal data.