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New SMT-based algorithm learns weighted automata efficiently

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new SMT-based active learning algorithm for nondeterministic weighted automata (WFAs). This method offers a practical and robust alternative to existing techniques, producing minimal WFAs and demonstrating strong performance in experimental evaluations. The algorithm is parametric and guaranteed to produce minimal WFAs if it terminates, with a proven termination condition for finite semirings. AI

IMPACT Introduces a novel algorithm for learning weighted automata, potentially improving efficiency and size of learned models in formal language processing tasks.

RANK_REASON The cluster contains a new academic paper detailing a novel algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SMT-based algorithm learns weighted automata efficiently

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The cluster contains a new academic paper detailing a novel algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandra Silva ·

    SMT-Based Active Learning of Weighted Automata

    We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-style methods. Our algorithm is parametric in a given semiring and, if it terminates, guaranteed to produce minimal WFAs. We prove pa…

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