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Demonstrating Learning of Register Automata

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Abstract

We will demonstrate the impact of the integration of our most recently developed learning technology for inferring Register Automata into the LearnLib, our framework for active automata learning. This will not only illustrate the unique power of Register Automata, which allows one to faithfully model data independent systems, but also the ease of enhancing the LearnLib with new functionality.

This work is supported by the European FP 7 project CONNECT (IST 231167).

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Author information

Authors and Affiliations

  1. Chair for Programming Systems, Technical University Dortmund, Dortmund, D-44227, Germany

    Maik Merten, Falk Howar & Bernhard Steffen

  2. Dept. of Information Technology, Uppsala University, Sweden

    Sofia Cassel & Bengt Jonsson

Authors
  1. Maik Merten

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  2. Falk Howar

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  3. Bernhard Steffen

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  4. Sofia Cassel

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  5. Bengt Jonsson

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Editor information

Editors and Affiliations

  1. University of California at Santa Cruz, 1156 High Street, 95064, Santa Cruz, CA, USA

    Cormac Flanagan

  2. Fakultät für Ingenieurwesen, Abteilung für Informatik und Angewandte Kognitionswissenschaft, Universität Duisburg-Essen, Lotharstraße 65, 47057, Duisburg, Germany

    Barbara König

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© 2012 Springer-Verlag Berlin Heidelberg

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Merten, M., Howar, F., Steffen, B., Cassel, S., Jonsson, B. (2012). Demonstrating Learning of Register Automata. In: Flanagan, C., König, B. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2012. Lecture Notes in Computer Science, vol 7214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28756-5_32

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