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

^*Corresponding author for this work

Institute of Information Systems

9 Citations (Scopus)

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.

Original language	English
Title of host publication	Canadian AI 2019: Advances in Artificial Intelligence
Editors	Marie-Jean Meurs, Frank Rudzicz
Number of pages	7
Volume	11489 LNAI
Publisher	Springer, Cham
Publication date	24.04.2019
Pages	380-386
ISBN (Print)	978-3-030-18304-2
ISBN (Electronic)	978-3-030-18305-9
DOIs	https://doi.org/10.1007/978-3-030-18305-9_33
Publication status	Published - 24.04.2019
Event	32nd Canadian Conference on Artificial Intelligence - Kingston, Canada Duration: 28.05.2019 → 31.05.2019

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure

Research Areas and Centers

Centers: Center for Artificial Intelligence Luebeck (ZKIL)
Research Area: Intelligent Systems

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

Cite this

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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",

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

language = "English",

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series = "Lecture Notes in Computer Science",

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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 (eds), Canadian AI 2019: Advances in Artificial Intelligence. vol. 11489 LNAI, Lecture Notes in Computer Science, vol. 11489 LNAI, Springer, Cham, pp. 380-386, 32nd Canadian Conference on Artificial Intelligence , Kingston, Ontario, Canada, 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. ed. / Marie-Jean Meurs; Frank Rudzicz. Vol. 11489 LNAI Springer, Cham, 2019. p. 380-386 (Lecture Notes in Computer Science; Vol. 11489 LNAI).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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.

UR - https://www.scopus.com/pages/publications/85066159661

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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