Towards lifted maximum expected utility

Marcel Gehrke; Tanya Braun; Ralf Möller; Alexander Waschkau; Christoph Strumann; Jost Steinhäuser

Towards lifted maximum expected utility

Marcel Gehrke, Tanya Braun, Ralf Möller, Alexander Waschkau, Christoph Strumann, Jost Steinhäuser

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 language	English
Title of host publication	1st Joint Workshop on AI in Health
Number of pages	4
Volume	2142
Publisher	CEUR-WS.org
Publication date	01.07.2018
Pages	93-96
Publication status	Published - 01.07.2018
Event	1st Joint Workshop on AI in Health - Stockholm, Sweden Duration: 13.07.2018 → 14.07.2018 Conference number: 138105

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure

DFG Research Classification Scheme

4.43-01 Theoretical Computer Science

Access to Document

https://www.ifis.uni-luebeck.de/uploads/tx_wapublications/lmeu_public_01.pdf

Cite this

@inproceedings{c5c1cff772ec494493d4cdeab0010bb4,

title = "Towards lifted maximum expected utility",

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. {\textcopyright} CEUR-WS. All rights reserved.",

author = "Marcel Gehrke and Tanya Braun and Ralf M{\"o}ller and Alexander Waschkau and Christoph Strumann and Jost Steinh{\"a}user",

year = "2018",

month = jul,

day = "1",

language = "English",

volume = "2142",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

pages = "93--96",

booktitle = "1st Joint Workshop on AI in Health",

note = "1st Joint Workshop on AI in Health ; Conference date: 13-07-2018 Through 14-07-2018",

}

Gehrke, M, Braun, T, Möller, R, Waschkau, A, Strumann, C & Steinhäuser, J 2018, Towards lifted maximum expected utility. in 1st Joint Workshop on AI in Health. vol. 2142, CEUR Workshop Proceedings, vol. 2142, CEUR-WS.org, pp. 93-96, 1st Joint Workshop on AI in Health, Stockholm, Sweden, 13.07.18. <https://www.ifis.uni-luebeck.de/uploads/tx_wapublications/lmeu_public_01.pdf>

TY - GEN

T1 - Towards lifted maximum expected utility

AU - Gehrke, Marcel

AU - Braun, Tanya

AU - Möller, Ralf

AU - Waschkau, Alexander

AU - Strumann, Christoph

AU - Steinhäuser, Jost

N1 - Conference code: 138105

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85050910863

M3 - Conference contribution

VL - 2142

T3 - CEUR Workshop Proceedings

SP - 93

EP - 96

BT - 1st Joint Workshop on AI in Health

PB - CEUR-WS.org

T2 - 1st Joint Workshop on AI in Health

Y2 - 13 July 2018 through 14 July 2018

ER -

Towards lifted maximum expected utility

Abstract

UN SDGs

DFG Research Classification Scheme

Access to Document

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