An SMT-based active learning algorithm learns minimal nondeterministic weighted automata over arbitrary semirings, with partial correctness proofs, a sufficient termination condition, and experiments showing smaller models and fewer queries than baselines.
In: Sem- pere, J.M., García, P
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An L#-inspired active learning algorithm learns minimal separating DFAs for disjoint languages when one exists and outperforms prior methods on random and industrial benchmarks.
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SMT-Based Active Learning of Weighted Automata
An SMT-based active learning algorithm learns minimal nondeterministic weighted automata over arbitrary semirings, with partial correctness proofs, a sufficient termination condition, and experiments showing smaller models and fewer queries than baselines.
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An $L^{\#}$ Based Algorithm for Active Learning of Minimal Separating Automata
An L#-inspired active learning algorithm learns minimal separating DFAs for disjoint languages when one exists and outperforms prior methods on random and industrial benchmarks.