Exploring Unknown Universes in Probabilistic Relational Models

Tanya Braun*, Ralf Möller

*Corresponding author for this work


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.

Original languageEnglish
Title of host publicationAI 2019: AI 2019: Advances in Artificial Intelligence
EditorsJixue Liu, James Bailey
Number of pages13
Volume11919 LNAI
PublisherSpringer, Cham
Publication date25.11.2019
ISBN (Print)978-3-030-35287-5
ISBN (Electronic)978-3-030-35288-2
Publication statusPublished - 25.11.2019
Event32nd Australasian Joint Conference on Artificial Intelligence - Adelaide, Australia
Duration: 02.12.201905.12.2019
Conference number: 234489

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems


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