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Communication Dans Un Congrès Année : 2023

Inference of over-constrained NFA of size k + 1 to efficiently and systematically derive NFA of size k for grammar learning

Résumé

Grammatical inference involves learning a formal grammar as a finite state machine or set of rewrite rules. This paper focuses on inferring Nondeterministic Finite Automata (NFA) from a given sample of words: the NFA must accept some words, and reject others. Our approach is unique in that it addresses the question of whether or not a finite automaton of size k exists for a given sample by using an overconstrained model of size k + 1. Additionally, our method allows for the identification of the automaton of size k when it exists. While the concept may seem straightforward, the effectiveness of this approach is demonstrated through the results of our experiments.
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Dates et versions

hal-04199534 , version 1 (07-09-2023)

Identifiants

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Tomasz Jastrząb, Frédéric Lardeux, Eric Monfroy. Inference of over-constrained NFA of size k + 1 to efficiently and systematically derive NFA of size k for grammar learning. International Conference on Computational Science (ICCS), Jul 2023, Prague, Czech Republic. pp.134-147, ⟨10.1007/978-3-031-35995-8_10⟩. ⟨hal-04199534⟩

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