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.
OriginalspracheEnglisch
TitelAI 2018: Advances in Artificial Intelligence
Redakteure/-innenTanja Mitrovic, Bing Xue, Xiaodong Li
Seitenumfang14
Band11320
ErscheinungsortCham
Herausgeber (Verlag)Springer International Publishing
Erscheinungsdatum10.11.2018
Seiten487-500
ISBN (Print)978-3-030-03990-5
ISBN (elektronisch)978-3-030-03991-2
DOIs
PublikationsstatusVeröffentlicht - 10.11.2018
Veranstaltung31st Australasian Joint Conference on Artificial Intelligence
- Wellington, Niger
Dauer: 11.12.201814.12.2018
https://ecs.victoria.ac.nz/Events/AI2018/WebHome#gallery

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

DFG-Fachsystematik

  • 409-01 Theoretische Informatik

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