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Constrained Training of Recurrent Neural Networks for Automata Learning

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Software Engineering and Formal Methods (SEFM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13550))

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Abstract

In this paper, we present a novel approach to learning finite automata with the help of recurrent neural networks. Our goal is not only to train a neural network that predicts the observable behavior of an automaton but also to learn its structure, including the set of states and transitions. In contrast to previous work, we constrain the training with a specific regularization term. We evaluate our approach with standard examples from the automata learning literature, but also include a case study of learning the finite-state models of real Bluetooth Low Energy protocol implementations. The results show that we can find an appropriate architecture to learn the correct automata in all considered cases.

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Notes

  1. 1.

    The actual corresponding input, resp. output, symbol is obtained from the input, resp. output, symbol alphabet through an appropriate indexed mapping. For simplicity, we don’t show this mapping here.

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Acknowledgement

This work was collaboratively done in the TU Graz LEAD project Dependable Internet of Things in Adverse Environments project, the LearnTwins project funded by FFG (Österreichische Forschungsförderungsgesellschaft) under grant 880852, and the “University SAL Labs” initiative of Silicon Austria Labs (SAL) and its Austrian partner universities for applied fundamental research for electronic based systems.

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Authors and Affiliations

  1. Institute of Software Technology, Graz University of Technology, Graz, Austria

    Bernhard K. Aichernig, Andrea Pferscher & Martin Tappler

  2. AIT Austrian Institute of Technology, Vienna, Austria

    Sandra König, Cristinel Mateis & Dominik Schmidt

  3. Silicon Austria Labs, TU Graz - SAL DES Lab, Graz, Austria

    Martin Tappler

Authors
  1. Bernhard K. Aichernig
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  2. Sandra König
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  3. Cristinel Mateis
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  4. Andrea Pferscher
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  5. Dominik Schmidt
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  6. Martin Tappler
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Corresponding author

Correspondence to Andrea Pferscher .

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Editors and Affiliations

  1. Humboldt-Universität zu Berlin, Berlin, Germany

    Bernd-Holger Schlingloff

  2. Beijing Jiaotong University, Beijing, China

    Ming Chai

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Aichernig, B.K., König, S., Mateis, C., Pferscher, A., Schmidt, D., Tappler, M. (2022). Constrained Training of Recurrent Neural Networks for Automata Learning. In: Schlingloff, BH., Chai, M. (eds) Software Engineering and Formal Methods. SEFM 2022. Lecture Notes in Computer Science, vol 13550. Springer, Cham. https://doi.org/10.1007/978-3-031-17108-6_10

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  • DOI: https://doi.org/10.1007/978-3-031-17108-6_10

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