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.
|Title of host publication||Artificial Intelligence in Health|
|Editors||Fernando Koch, Andrew Koster, David Riaño, Sara Montagna, Michael Schumacher, Annette ten Teije, Christian Guttmann, Manfred Reichert, Isabelle Bichindaritz, Pau Herrero, Richard Lenz, Beatriz López, Cindy Marling, Clare Martin, Stefania Montani, Nirmalie Wiratunga|
|Number of pages||11|
|Place of Publication||Cham|
|Publisher||Springer International Publishing|
|Publication status||Published - 2019|