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2019 | OriginalPaper | Chapter

Lifted Temporal Maximum Expected Utility

Authors : Marcel Gehrke, Tanya Braun, Ralf Möller

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

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.

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Title: Lifted Temporal Maximum Expected Utility
Authors: Marcel Gehrke
Tanya Braun
Ralf Möller
Publisher: Springer International Publishing
Book: Advances in Artificial Intelligence
Print ISBN: 978-3-030-18304-2

Electronic ISBN: 978-3-030-18305-9

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-18305-9_33

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