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Scalable Learning of One-Counter Automata via State-Merging Algorithms

Authors Shibashis Guha , Anirban Majumdar , Prince Mathew , A.V. Sreejith

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LIPIcs.FSTTCS.2025.35.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Formal languages and automata theory
Keywords
  • active learning
  • passive learning
  • one-counter automata
  • RPNI

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Abstract

We propose One-counter Positive Negative Inference (OPNI), a passive learning algorithm for deterministic real-time one-counter automata (DROCA). Inspired by the RPNI algorithm for regular languages, OPNI constructs a DROCA consistent with any given valid sample set.
We further present a semi-algorithm for active learning of DROCA using OPNI, and provide an implementation of the approach. Our experimental results demonstrate that this approach scales more effectively than existing state-of-the-art algorithms. We also evaluate the performance of the proposed approach for learning visibly one-counter automata.

Cite As Get BibTex

Shibashis Guha, Anirban Majumdar, Prince Mathew, and A.V. Sreejith. Scalable Learning of One-Counter Automata via State-Merging Algorithms. In 45th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 360, pp. 35:1-35:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.FSTTCS.2025.35

Author Details

Shibashis Guha
  • Tata Institute of Fundamental Research, Mumbai, India
Anirban Majumdar
  • Independent researcher, Kolkata, India
Prince Mathew
  • Indian Institute of Technology Goa, Ponda, India
A.V. Sreejith
  • Indian Institute of Technology Goa, Ponda, India

Funding

Prince Mathew and A.V. Sreejith would like to thank CEFIPRA for the project IFC/ 6602-1/2022/77. A.V. Sreejith would also like to thank the DST Matrics grant for the project MTR/2022/000788.

Supplementary Materials

References

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