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arXiv Machine Learning By Tiago Ferreira, Kevin Batz, Alexandra Silva

SMT-Based Active Learning of Weighted Automata

Read the original on arXiv Machine Learning →

The paper introduces an SMT-based active learning algorithm for nondeterministic weighted finite automata (WFAs), offering a practical alternative to traditional Hankel/L*-style methods. The algorithm is parametric over a chosen semiring and, upon termination, guarantees the production of minimal WFAs. Experiments demonstrate that it learns numerous minimal WFAs over both finite and infinite semirings, outperforming a naive baseline and competing with state‑of‑the‑art algorithms while yielding smaller automata and requiring less teacher interaction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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