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International Conference on Algebraic Informatics

CAI 2007: Algebraic Informatics pp 218–235Cite as

Learning Deterministically Recognizable Tree Series — Revisited

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4728))

Abstract

We generalize a learning algorithm originally devised for deterministic all-accepting weighted tree automata (wta) to the setting of arbitrary deterministic wta. The learning is exact, supervised, and uses an adapted minimal adequate teacher; a learning model introduced by Angluin. Our algorithm learns a minimal deterministic wta that recognizes the taught tree series and runs in polynomial time in the size of that wta and the size of the provided counterexamples. Compared to the original algorithm, we show how to handle non-final states in the learning process; this problem was posed as an open problem in [Drewes, Vogler: Learning Deterministically Recognizable Tree Series, J. Autom. Lang. Combin. 2007].

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Authors and Affiliations

  1. Institute of Theoretical Computer Science, Faculty of Computer Science, Technische Universität Dresden,  

    Andreas Maletti

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  1. Andreas Maletti
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Symeon Bozapalidis George Rahonis

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© 2007 Springer-Verlag Berlin Heidelberg

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Maletti, A. (2007). Learning Deterministically Recognizable Tree Series — Revisited. In: Bozapalidis, S., Rahonis, G. (eds) Algebraic Informatics. CAI 2007. Lecture Notes in Computer Science, vol 4728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75414-5_14

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  • DOI: https://doi.org/10.1007/978-3-540-75414-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75413-8

  • Online ISBN: 978-3-540-75414-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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