Adaptive Inference on Probabilistic Relational Models

Tanya Braun, Ralf Möller

Abstract

Standard approaches for inference in probabilistic relational models include lifted variable elimination (LVE) for single queries. To efficiently handle multiple queries, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a model, employing LVE as a subroutine in its steps. Adaptive inference concerns efficient inference under changes in a model. If the model changes, LJT restarts, possibly unnecessarily dumping information. The purpose of this paper is twofold, (i) to adapt the cluster representation to incremental changes, and (ii) to transform LJT into an adaptive version, enabling LJT to preserve as much computations as possible. Adaptive LJT fast reaches the point of answering queries again after changes, which is especially important for time-critical applications or online query answering.
Original languageEnglish
Title of host publicationAI 2018: Advances in Artificial Intelligence
EditorsTanja Mitrovic, Bing Xue, Xiaodong Li
Number of pages14
Volume11320
Place of PublicationCham
PublisherSpringer International Publishing
Publication date10.11.2018
Pages487-500
ISBN (Print)978-3-030-03990-5
ISBN (Electronic)978-3-030-03991-2
DOIs
Publication statusPublished - 10.11.2018
Event31st Australasian Joint Conference on Artificial Intelligence
- Wellington, Niger
Duration: 11.12.201814.12.2018
https://ecs.victoria.ac.nz/Events/AI2018/WebHome#gallery

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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