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Automata Learning with an Incomplete Teacher (Artifact)

Authors Mark Moeller , Thomas Wiener, Alaia Solko-Breslin , Caleb Koch, Nate Foster , Alexandra Silva

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Artifact Description

DARTS.9.2.21.pdf
  • Filesize: 0.55 MB
  • 3 pages

Document Identifiers

Author Details

Mark Moeller
  • Cornell University, Ithaca, NY, USA
Thomas Wiener
  • Cornell University, Ithaca, NY, USA
Alaia Solko-Breslin
  • University of Pennsylvania, Philadelphia, PA, USA
Caleb Koch
  • Stanford University, Stanford, CA, USA
Nate Foster
  • Cornell University, Ithaca, NY, USA
Alexandra Silva
  • Cornell University, Ithaca, NY, USA

Funding

This material is based upon work supported by the Office of Naval Research under Contract No. N68335-22-C-0411.

Acknowledgements

We thank Marijn Heule, Martin Leucker, and Arlindo Oliveira for their efforts in providing us access to their code and benchmarks. We also thank Akshat Singh and Sheetal Athrey, with whom this project began as an undergraduate research project.

Cite As Get BibTex

Mark Moeller, Thomas Wiener, Alaia Solko-Breslin, Caleb Koch, Nate Foster, and Alexandra Silva. Automata Learning with an Incomplete Teacher (Artifact). In Special Issue of the 37th European Conference on Object-Oriented Programming (ECOOP 2023). Dagstuhl Artifacts Series (DARTS), Volume 9, Issue 2, pp. 21:1-21:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/DARTS.9.2.21

Artifact

  MD5 Sum: 4365265d0d7390aa915d8aee84cdd1cc (Get MD5 Sum)

Artifact Evaluation Policy

The artifact has been evaluated as described in the ECOOP 2023 Call for Artifacts and the ACM Artifact Review and Badging Policy

Abstract

We provide an implementation of the automata learning software described in the associated ECOOP article. In particular, the artifact is a Docker image with the source code for nerode and nerode-learn, along with the scripts and benchmark inputs needed to reproduce the experiments described in the paper.

Subject Classification

ACM Subject Classification
  • Theory of computation → Active learning
Keywords
  • Finite Automata
  • Active Learning
  • SMT Solvers

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Related Article

  • Mark Moeller, Thomas Wiener, Alaia Solko-Breslin, Caleb Koch, Nate Foster, and Alexandra Silva, "Automata Learning with an Incomplete Teacher", in 37th European Conference on Object-Oriented Programming (ECOOP 2023), LIPIcs, Vol. 263, pp. 21:1-21:30, 2023. https://doi.org/10.4230/LIPIcs.ECOOP.2023.21

References

  1. Leonardo Mendonça de Moura and Nikolaj S. Bjørner. Z3: an efficient SMT solver. In C. R. Ramakrishnan and Jakob Rehof, editors, Tools and Algorithms for the Construction and Analysis of Systems, 14th International Conference, TACAS 2008, Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2008, Budapest, Hungary, March 29-April 6, 2008. Proceedings, volume 4963 of Lecture Notes in Computer Science, pages 337-340. Springer, 2008. URL: https://doi.org/10.1007/978-3-540-78800-3_24.
  2. Mina Lee, Sunbeom So, and Hakjoo Oh. Synthesizing regular expressions from examples for introductory automata assignments. In Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, GPCE 2016, pages 70-80, New York, NY, USA, 2016. Association for Computing Machinery. URL: https://doi.org/10.1145/2993236.2993244.
  3. Arlindo Oliveira and J.P.M. Silva. Efficient algorithms for the inference of minimum size dfas. Machine Learning, 44, July 2001. Google Scholar
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