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Learning Mealy Machines with One Timer

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12638)

Abstract

We present Mealy machines with a single timer (MM1Ts), a class of models that is both sufficiently expressive to describe the real-time behavior of many realistic applications, and can be learned efficiently. We show how learning algorithms for MM1Ts can be obtained via a reduction to the problem of learning Mealy machines. We describe an implementation of an MM1T learner on top of LearnLib, and compare its performance with recent algorithms proposed by Aichernig et al. and An et al. on several realistic benchmarks.

This work was supported by the Austrian Research Promotion Agency (FFG) through project TRUSTED (867558), Graz University of Technology’s LEAD project “Dependable Internet of Things in Adverse Environments” and by Radboud University’s NWO TOP project 612.001.852 “Grey-box learning of Interfaces for Refactoring Legacy Software (GIRLS)”.

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Notes

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    https://extgit.iaik.tugraz.at/scos/scos.sources/LearningMMTs.

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Acknowledgement

We would like to thank Andrea Pferscher and Miaomiao Zhang for help with running the benchmarks on their tools [1, 3].

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Correspondence to Frits Vaandrager , Roderick Bloem or Masoud Ebrahimi .

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Vaandrager, F., Bloem, R., Ebrahimi, M. (2021). Learning Mealy Machines with One Timer. In: Leporati, A., Martín-Vide, C., Shapira, D., Zandron, C. (eds) Language and Automata Theory and Applications. LATA 2021. Lecture Notes in Computer Science(), vol 12638. Springer, Cham. https://doi.org/10.1007/978-3-030-68195-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-68195-1_13

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