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Active Learning of Deterministic Transducers with Outputs in Arbitrary Monoids

Author Quentin Aristote

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Quentin Aristote
  • École Normale Supérieure de Paris, PSL University, France
  • Université Paris Cité, CNRS, Inria, IRIF, F-75013, Paris, France

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Quentin Aristote. Active Learning of Deterministic Transducers with Outputs in Arbitrary Monoids. In 32nd EACSL Annual Conference on Computer Science Logic (CSL 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 288, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CSL.2024.11

Abstract

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Transducers
Keywords
  • transducers
  • monoids
  • active learning
  • category theory

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