Learning automata and transducers: a categorical approach

Apprentissage d'automates et de transducteurs: une approche catégorique

Thomas Colcombet (1, 2) , Daniela Petrişan (1) , Riccardo Stabile (3)

1 IRIF (UMR_8243) - Institut de Recherche en Informatique Fondamentale
2 CNRS - Centre National de la Recherche Scientifique
3 Dipartimento de Matematica [Milano]

In this paper, we present a categorical approach to learning automata over words, in the sense of the L∗-algorithm of Angluin. This yields a new generic L∗-like algorithm which can be instantiated for learning deterministic automata, automata weighted over fields, as well as subsequential transducers. The generic nature of our algorithm is obtained by adopting an approach in which automata are simply functors from a particular category representing words to a “computation category”. We establish that the sufficient properties for yielding the existence of minimal automata (that were disclosed in a previous paper), in combination with some additional hypotheses relative to termination, ensure the correctness of our generic algorithm.

Mots clés: automata; transducer; learning; category

Domaines: Informatique [cs]; Théorie et langage formel [cs.FL]

Thomas Colcombet, Daniela Petrişan, Riccardo Stabile. Learning automata and transducers: a categorical approach. 29th {EACSL} Annual Conference on Computer Science Logic, CSL 2021, Jan 2021, Ljubljana, Slovenia. ⟨10.4230/LIPIcs.CSL.2021.15⟩. ⟨hal-03106961⟩