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Lifted Junction Tree Algorithm

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KI 2016: Advances in Artificial Intelligence (KI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9904))

Abstract

We look at probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach for inference is lifted variable elimination. To benefit from the advantages of the junction tree algorithm for inference in the first-order setting, we transfer the idea of lifting to the junction tree algorithm.

Our lifted junction tree algorithm aims at reducing computations by introducing first-order junction trees that compactly represent symmetries. First experiments show that we speed up the computation time compared to the propositional version. When querying for multiple marginals, the lifted junction tree algorithm performs better than using lifted VE to infer each marginal individually.

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Author information

Authors and Affiliations

  1. Institute of Information Systems, Universität Zu Lübeck, Lübeck, Germany

    Tanya Braun & Ralf Möller

Authors
  1. Tanya Braun
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  2. Ralf Möller
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Corresponding author

Correspondence to Tanya Braun .

Editor information

Editors and Affiliations

  1. Alpen-Adria Universität Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  2. University of Basel, Basel, Switzerland

    Malte Helmert

  3. Technische Universität Graz, Graz, Austria

    Franz Wotawa

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© 2016 Springer International Publishing AG

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Braun, T., Möller, R. (2016). Lifted Junction Tree Algorithm. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-46073-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46072-7

  • Online ISBN: 978-3-319-46073-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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