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.

Originalsprache	Englisch
Titel	1st Joint Workshop on AI in Health
Seitenumfang	4
Band	2142
Herausgeber (Verlag)	CEUR-WS.org
Erscheinungsdatum	01.07.2018
Seiten	93-96
Publikationsstatus	Veröffentlicht - 01.07.2018
Veranstaltung	1st Joint Workshop on AI in Health - Stockholm, Schweden Dauer: 13.07.2018 → 14.07.2018 Konferenznummer: 138105

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Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 3 – Gesundheit und Wohlergehen
SDG 8 – Angemessene Arbeitsbedingungen und wirtschaftliches Wachstum
SDG 9 – Industrie, Innovation und Infrastruktur

DFG-Fachsystematik

4.43-01 Theoretische Informatik

Dokumente

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

Weitere Links

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

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@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. Bd. 2142, CEUR Workshop Proceedings, Bd. 2142, CEUR-WS.org, S. 93-96, 1st Joint Workshop on AI in Health, Stockholm, Schweden, 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-Fachsystematik

Dokumente

Weitere Links

Fingerprint

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