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Deterministic Weighted Automata Under Partial Observability

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Logics in Artificial Intelligence (JELIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14281))

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Abstract

Weighted automata is a basic tool for specification in quantitative verification, which allows to express quantitative features of analysed systems such as resource consumption. Quantitative specification can be assisted by automata learning as there are classic results on Angluin-style learning of weighted automata. The existing work assumes perfect information about the values returned by the target weighted automaton. In assisted synthesis of a quantitative specification, knowledge of the exact values is a strong assumption and may be infeasible. In our work, we address this issue by introducing a new framework of partially-observable deterministic weighted automata, in which weighted automata return intervals containing the computed values of words instead of the exact values. We study the basic properties of this framework with the particular focus on the challenges of active learning partially-observable deterministic weighted automata.

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Acknowledgements

This work was supported by the National Science Centre (NCN), Poland under grant 2020/39/B/ST6/00521.

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Authors and Affiliations

  1. University of Wrocław, Wrocław, Poland

    Jakub Michaliszyn & Jan Otop

Authors
  1. Jakub Michaliszyn
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  2. Jan Otop
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Corresponding author

Correspondence to Jan Otop .

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Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Sarah Gaggl

  2. Artificial Intelligence Research Institute - IIIA CSIC, Barcelona, Spain

    Maria Vanina Martinez

  3. Umeå University, Umeå, Sweden

    Magdalena Ortiz

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Michaliszyn, J., Otop, J. (2023). Deterministic Weighted Automata Under Partial Observability. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_52

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  • DOI: https://doi.org/10.1007/978-3-031-43619-2_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43618-5

  • Online ISBN: 978-3-031-43619-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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