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Learning Deterministic Visibly Pushdown Automata Under Accessible Stack

Authors Jakub Michaliszyn , Jan Otop

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Author Details

Jakub Michaliszyn
  • University of Wrocław, Poland
Jan Otop
  • University of Wrocław, Poland

Funding

This work was supported by the National Science Centre (NCN), Poland under grant 2020/39/B/ST6/00521.

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Jakub Michaliszyn and Jan Otop. Learning Deterministic Visibly Pushdown Automata Under Accessible Stack. In 47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 241, pp. 74:1-74:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.MFCS.2022.74

Abstract

We study the problem of active learning deterministic visibly pushdown automata. We show that in the classical L^*-setting, efficient active learning algorithms are not possible. To overcome this difficulty, we propose the accessible stack setting, where the algorithm has the read and write access to the stack. In this setting, we show that active learning can be done in polynomial time in the size of the target automaton and the counterexamples provided by the teacher. As counterexamples of exponential size are inevitable, we consider an algorithm working with words in a compressed representation via (visibly) Straight-Line Programs. Employing compression allows us to obtain an algorithm where the teacher and the learner work in time polynomial in the size of the target automaton alone.

Subject Classification

ACM Subject Classification
  • Theory of computation → Formal languages and automata theory
Keywords
  • visibly pushdown automata
  • automata inference
  • minimization

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