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.
Communications of the ACM60, 86–95 (2017)
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N-gram models equipped with a dynamic promotion ensemble match or exceed the accuracy of neural networks for next-activity prediction in event logs while using substantially fewer resources.
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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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Promoting Simple Agents: Ensemble Methods for Event-Log Prediction
N-gram models equipped with a dynamic promotion ensemble match or exceed the accuracy of neural networks for next-activity prediction in event logs while using substantially fewer resources.