Lifted Temporal Maximum Expected Utility

Marcel Gehrke; Tanya Braun; Ralf Möller

doi:10.1007/978-3-030-18305-9_33

Lifted Temporal Maximum Expected Utility

Marcel Gehrke^*, Tanya Braun, Ralf Möller

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Informationssysteme

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.

Originalsprache	Englisch
Titel	Canadian AI 2019: Advances in Artificial Intelligence
Redakteure/-innen	Marie-Jean Meurs, Frank Rudzicz
Seitenumfang	7
Band	11489 LNAI
Herausgeber (Verlag)	Springer, Cham
Erscheinungsdatum	24.04.2019
Seiten	380-386
ISBN (Print)	978-3-030-18304-2
ISBN (elektronisch)	978-3-030-18305-9
DOIs	https://doi.org/10.1007/978-3-030-18305-9_33
Publikationsstatus	Veröffentlicht - 24.04.2019
Veranstaltung	32nd Canadian Conference on Artificial Intelligence - Kingston, Kanada Dauer: 28.05.2019 → 31.05.2019

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title = "Lifted Temporal Maximum Expected Utility",

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.",

author = "Marcel Gehrke and Tanya Braun and Ralf M{\"o}ller",

note = "Funding Information: This research originated from the Big Data project being part of Joint Lab 1, funded by Cisco Systems Germany, at the centre COPICOH, University of L{\"u}beck. Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 32nd Canadian Conference on Artificial Intelligence , Canadian AI 2019 ; Conference date: 28-05-2019 Through 31-05-2019",

year = "2019",

month = apr,

day = "24",

doi = "10.1007/978-3-030-18305-9_33",

language = "English",

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volume = "11489 LNAI",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

pages = "380--386",

editor = "Meurs, {Marie-Jean } and Rudzicz, {Frank }",

booktitle = "Canadian AI 2019: Advances in Artificial Intelligence",

}

Gehrke, M, Braun, T & Möller, R 2019, Lifted Temporal Maximum Expected Utility. in M-J Meurs & F Rudzicz (Hrsg.), Canadian AI 2019: Advances in Artificial Intelligence. Bd. 11489 LNAI, Lecture Notes in Computer Science, Bd. 11489 LNAI, Springer, Cham, S. 380-386, 32nd Canadian Conference on Artificial Intelligence , Kingston, Ontario, Kanada, 28.05.19. https://doi.org/10.1007/978-3-030-18305-9_33

Lifted Temporal Maximum Expected Utility. / Gehrke, Marcel; Braun, Tanya; Möller, Ralf.
Canadian AI 2019: Advances in Artificial Intelligence. Hrsg. / Marie-Jean Meurs; Frank Rudzicz. Band 11489 LNAI Springer, Cham, 2019. S. 380-386 (Lecture Notes in Computer Science; Band 11489 LNAI).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Lifted Temporal Maximum Expected Utility

AU - Gehrke, Marcel

AU - Braun, Tanya

AU - Möller, Ralf

N1 - Funding Information: This research originated from the Big Data project being part of Joint Lab 1, funded by Cisco Systems Germany, at the centre COPICOH, University of Lübeck. Publisher Copyright: © 2019, Springer Nature Switzerland AG. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2019/4/24

Y1 - 2019/4/24

N2 - 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.

AB - 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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U2 - 10.1007/978-3-030-18305-9_33

DO - 10.1007/978-3-030-18305-9_33

M3 - Conference contribution

SN - 978-3-030-18304-2

VL - 11489 LNAI

T3 - Lecture Notes in Computer Science

SP - 380

EP - 386

BT - Canadian AI 2019: Advances in Artificial Intelligence

A2 - Meurs, Marie-Jean

A2 - Rudzicz, Frank

PB - Springer, Cham

T2 - 32nd Canadian Conference on Artificial Intelligence

Y2 - 28 May 2019 through 31 May 2019

ER -

Lifted Temporal Maximum Expected Utility

Abstract

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