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Parallel Induction of Nondeterministic Finite Automata

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Parallel Processing and Applied Mathematics (PPAM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9573))

Abstract

The induction of a minimal nondeterministic finite automaton (NFA) consistent with a given set of examples and counterexamples, which is known to be computationally hard, is discussed. The paper is an extension to the novel approach of transforming the problem of NFA induction into the integer nonlinear programming (INLP) problem. An improved formulation of the problem is proposed along with the two parallel algorithms to solve it. The methods for the distribution of tasks among processors along with distributed termination detection are presented. The experimental results for selected benchmarks are also reported.

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Notes

  1. 1.

    The implementations of the RPNI and EDSM algorithms were taken from the open source project of grammatical inference tools (gitoolbox) available at https://code.google.com/p/gitoolbox. The toolbox is described in [13].

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Acknowledgment

Computations were carried out using the computer cluster Galera at the Academic Computer Center in Gdańsk (http://task.gda.pl/kdm) and using the computer cluster Ziemowit (http://ziemowit.hpc.polsl.pl) funded by the Silesian BIO-FARMA project No. POIG.02.01.00-00-166/08 in the Computational Biology and Bioinformatics Laboratory of the Biotechnology Centre in the Silesian University of Technology, Gliwice, Poland.

This research was supported by Grant No. DEC-2011/03/B/ST6/01588 from National Science Center of Poland and the Institute of Informatics research grant no. 525/RAU2/2014/9.

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Correspondence to Tomasz Jastrzab .

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Jastrzab, T., Czech, Z.J., Wieczorek, W. (2016). Parallel Induction of Nondeterministic Finite Automata. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2015. Lecture Notes in Computer Science(), vol 9573. Springer, Cham. https://doi.org/10.1007/978-3-319-32149-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-32149-3_24

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