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Learning Automata and Transducers: A Categorical Approach

Authors Thomas Colcombet , Daniela Petrişan , Riccardo Stabile



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Author Details

Thomas Colcombet
  • IRIF, CNRS, Paris, France
Daniela Petrişan
  • IRIF, Université de Paris, France
Riccardo Stabile
  • Università degli Studi di Milano, Dipartimento di Matematica, Italy

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Thomas Colcombet, Daniela Petrişan, and Riccardo Stabile. Learning Automata and Transducers: A Categorical Approach. In 29th EACSL Annual Conference on Computer Science Logic (CSL 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 183, pp. 15:1-15:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.CSL.2021.15

Abstract

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Algebraic language theory
  • Theory of computation → Transducers
Keywords
  • Automata
  • transducer
  • learning
  • category

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