Functorial approach to minimizing and learning deterministic transducers with outputs in arbitrary monoids

Quentin Aristote (1, 2)

1 ENS-PSL - École normale supérieure - Paris
2 IRIF (UMR_8243) - Institut de Recherche en Informatique Fondamentale

We study monoidal transducers, transition systems arising as deterministic automata whose transitions also produce outputs in an arbitrary monoid, for instance allowing outputs to commute or to cancel out. We use the categorical framework for minimization and learning of Colcombet, Petrişan and Stabile to recover the notion of minimal transducer recognizing a language, and give necessary and sufficient conditions on the output monoid for this minimal transducer to exist and be unique (up to isomorphism). The categorical framework then provides an abstract algorithm for learning it using membership and equivalence queries, and we discuss practical aspects of this algorithm's implementation. We also extend the framework with a categorical algorithm for minimizing transition systems, whose instantiation retrieves the algorithm for minimizing monoidal transducers but also extends the class of output monoids for which this algorithm is valid.

Mots clés: transducers; active learning; category theory

Domaines: Théorie et langage formel [cs.FL]

Quentin Aristote. Functorial approach to minimizing and learning deterministic transducers with outputs in arbitrary monoids. 2024. ⟨hal-04172251v3⟩