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Exploring Unknown Universes in Probabilistic Relational Models

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AI 2019: Advances in Artificial Intelligence (AI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11919))

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Abstract

Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for tractable inference. Universes may not always be known, though, or may only described by assumptions such as “small universes are more likely”. Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.

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Authors and Affiliations

  1. University of 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 .

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Editors and Affiliations

  1. University of South Australia, Adelaide, SA, Australia

    Jixue Liu

  2. The University of Melbourne, Melbourne, VIC, Australia

    James Bailey

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Braun, T., Möller, R. (2019). Exploring Unknown Universes in Probabilistic Relational Models. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-35288-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35287-5

  • Online ISBN: 978-3-030-35288-2

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