Lifted Junction Tree Algorithm

Tanya Braun, Ralf Möller


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.
Original languageEnglish
Title of host publicationKI 2016: Advances in Artificial Intelligence
EditorsGerhard Friedrich, Malte Helmert, Franz Wotawa
Number of pages13
Place of PublicationCham
PublisherSpringer International Publishing
Publication date08.09.2016
ISBN (Print)978-3-319-46072-7
ISBN (Electronic)978-3-319-46073-4
Publication statusPublished - 08.09.2016
Event39th German Conference on Artificial Intelligence - Klagenfurt, Austria
Duration: 26.09.201630.09.2016
Conference number: 181639

DFG Research Classification Scheme

  • 409-01 Theoretical Computer Science


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