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Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2018: KI 2018: Advances in Artificial Intelligence pp 24–37Cite as

Fusing First-Order Knowledge Compilation and the Lifted Junction Tree Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11117))

Abstract

Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowledge compilation (FOKC) based on weighted model counting. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model and LVE as a subroutine in its computations. For certain inputs, the implementation of LVE and, as a result, LJT ground parts of a model where FOKC runs without groundings. The purpose of this paper is to prepare LJT as a backbone for lifted query answering and to use any exact inference algorithm as subroutine. Fusing LJT and FOKC, by setting FOKC as a subroutine, allows us to compute answers faster than FOKC alone and LJT with LVE for certain inputs.

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Authors and Affiliations

  1. University of Lübeck, Lübeck, Germany

    Tanya Braun & Ralf Möller

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  1. Tanya Braun
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  2. Ralf Möller
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Correspondence to Tanya Braun .

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Editors and Affiliations

  1. TU Berlin, Berlin, Germany

    Frank Trollmann

  2. TU Dresden, Dresden, Germany

    Anni-Yasmin Turhan

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Braun, T., Möller, R. (2018). Fusing First-Order Knowledge Compilation and the Lifted Junction Tree Algorithm. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-00111-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00110-0

  • Online ISBN: 978-3-030-00111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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