Lifted Maximum Expected Utility

  • Marcel GehrkeEmail author
  • Tanya Braun
  • Ralf Möller
  • Alexander Waschkau
  • Christoph Strumann
  • Jost Steinhäuser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11326)


The lifted junction tree algorithm (LJT) answers multiple queries efficiently for relational models under uncertainties by building and then reusing a first-order cluster representation. We extend the underling model representation of LJT, which is called parameterised probabilistic model, to calculate a lifted solution to the maximum expected utility (MEU) problem. Specifically, this paper contributes (i) action and utility nodes for parameterised probabilistic models, resulting in parameterised probabilistic decision models and (ii) meuLJT, an algorithm to solve the MEU problem using parameterised probabilistic decision models efficiently, while also being able to answer multiple marginal queries.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information SystemsUniversity of LübeckLübeckGermany
  2. 2.Institute of Family MedicineUniversity Medical Center Schleswig-HolsteinLübeckGermany

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