Abstract

The lifted dynamic junction tree algorithm (LDJT) efficiently answers exact filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend the underling model of LDJT to provide means to calculate a lifted temporal solution to the maximum expected utility problem. © CEUR-WS. All rights reserved.
Original languageEnglish
Title of host publication1st Joint Workshop on AI in Health
Number of pages4
Volume2142
PublisherCEUR-WS.org
Publication date01.07.2018
Pages93-96
Publication statusPublished - 01.07.2018
Event1st Joint Workshop on AI in Health - Stockholm, Sweden
Duration: 13.07.201814.07.2018
Conference number: 138105

DFG Research Classification Scheme

  • 409-01 Theoretical Computer Science

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