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

Tanya Braun, Ralf Möller

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.
Original languageEnglish
Title of host publicationKI 2018: Advances in Artificial Intelligence
EditorsFrank Trollmann, Anni-Yasmin Turhan
Number of pages14
Volume11117
Place of PublicationCham
PublisherSpringer International Publishing
Publication date30.08.2018
Pages24-37
ISBN (Print)978-3-030-00110-0
ISBN (Electronic)978-3-030-00111-7
DOIs
Publication statusPublished - 30.08.2018
Event41st German Conference on Artificial Intelligence
- Berlin, Germany
Duration: 24.09.201828.09.2018
Conference number: 218679

Research Areas and Centers

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

DFG Research Classification Scheme

  • 409-01 Theoretical Computer Science

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