Lifted Temporal Maximum Expected Utility

Marcel Gehrke*, Tanya Braun, Ralf Möller

*Corresponding author for this work


The dynamic junction tree algorithm (LDJT) efficiently answers exact filtering and prediction queries for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. To also support sequential online decision making, we extend the underling model of LDJT with action and utility nodes, resulting in parameterised probabilistic dynamic decision models, and introduce meuLDJT to efficiently solve the exact lifted temporal maximum expected utility problem, while also answering marginal queries efficiently.

Original languageEnglish
Title of host publicationCanadian AI 2019: Advances in Artificial Intelligence
EditorsMarie-Jean Meurs, Frank Rudzicz
Number of pages7
Volume11489 LNAI
PublisherSpringer, Cham
Publication date24.04.2019
ISBN (Print)978-3-030-18304-2
ISBN (Electronic)978-3-030-18305-9
Publication statusPublished - 24.04.2019
Event32nd Canadian Conference on Artificial Intelligence - Kingston, Canada
Duration: 28.05.201931.05.2019

Research Areas and Centers

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


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