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Gray-Box Learning of Serial Compositions of Mealy Machines

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NASA Formal Methods (NFM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9690))

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Abstract

We study the following gray-box learning problem: Given the serial composition of two Mealy machines A and B, where A is known and B is unknown, the goal is to learn a model of B using only output and equivalence queries on the composed machine.

We introduce an algorithm that solves this problem, using at most |B| equivalence queries, independently of the size of A. We discuss its efficient implementation and evaluate the algorithm on existing benchmark sets as well as randomly-generated machines.

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Notes

  1. 1.

    The proofs for the theorems in this section are available at http://embedded.cs.uni-saarland.de/GrayBoxLearning/details.pdf.

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Authors and Affiliations

  1. Department of Computer Science, Saarland University, Saarbücken, Germany

    Andreas Abel & Jan Reineke

Authors
  1. Andreas Abel
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  2. Jan Reineke
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Correspondence to Andreas Abel .

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Editors and Affiliations

  1. University of Minnesota, Minneapolis, Minnesota, USA

    Sanjai Rayadurgam

  2. NASA Ames Research Center , Moffett Field, California, USA

    Oksana Tkachuk

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Abel, A., Reineke, J. (2016). Gray-Box Learning of Serial Compositions of Mealy Machines. In: Rayadurgam, S., Tkachuk, O. (eds) NASA Formal Methods. NFM 2016. Lecture Notes in Computer Science(), vol 9690. Springer, Cham. https://doi.org/10.1007/978-3-319-40648-0_21

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  • DOI: https://doi.org/10.1007/978-3-319-40648-0_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-40648-0

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