Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

Marcel Gehrke; Tanya Braun; Ralf Möller

doi:10.1007/978-3-030-00111-7_4

Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

Marcel Gehrke, Tanya Braun, Ralf Möller

Institute of Information Systems

2 Citations (Scopus)

Abstract

The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently 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. Unfortunately, a non-ideal elimination order can lead to unnecessary groundings.

Original language	English
Title of host publication	KI 2018: Advances in Artificial Intelligence
Editors	Frank Trollmann, Anni-Yasmin Turhan
Number of pages	8
Volume	11117
Place of Publication	Cham
Publisher	Springer International Publishing
Publication date	30.08.2018
Pages	38-45
ISBN (Print)	978-3-030-00110-0
ISBN (Electronic)	978-3-030-00111-7
DOIs	https://doi.org/10.1007/978-3-030-00111-7_4
Publication status	Published - 30.08.2018
Event	41st German Conference on Artificial Intelligence - Berlin, Germany Duration: 24.09.2018 → 28.09.2018 Conference number: 218679

DFG Research Classification Scheme

4.43-01 Theoretical Computer Science

UN SDGs

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

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10.1007/978-3-030-00111-7_4

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Gehrke, M., Braun, T., & Möller, R. (2018). Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm. In F. Trollmann, & A.-Y. Turhan (Eds.), KI 2018: Advances in Artificial Intelligence (Vol. 11117, pp. 38-45). (Lecture Notes in Computer Science; Vol. 11117). Springer International Publishing. https://doi.org/10.1007/978-3-030-00111-7_4

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title = "Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm",

abstract = "The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently 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. Unfortunately, a non-ideal elimination order can lead to unnecessary groundings.",

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

year = "2018",

month = aug,

day = "30",

doi = "10.1007/978-3-030-00111-7\_4",

language = "English",

isbn = "978-3-030-00110-0",

volume = "11117",

series = "Lecture Notes in Computer Science",

publisher = "Springer International Publishing",

pages = "38--45",

editor = "Frank Trollmann and Anni-Yasmin Turhan",

booktitle = "KI 2018: Advances in Artificial Intelligence",

note = "41st German Conference on Artificial Intelligence<br/>, KI 2018 ; Conference date: 24-09-2018 Through 28-09-2018",

}

Gehrke, M, Braun, T & Möller, R 2018, Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm. in F Trollmann & A-Y Turhan (eds), KI 2018: Advances in Artificial Intelligence. vol. 11117, Lecture Notes in Computer Science, vol. 11117, Springer International Publishing, Cham, pp. 38-45, 41st German Conference on Artificial Intelligence
, Berlin, Berlin, Germany, 24.09.18. https://doi.org/10.1007/978-3-030-00111-7_4

Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm. / Gehrke, Marcel; Braun, Tanya; Möller, Ralf.
KI 2018: Advances in Artificial Intelligence. ed. / Frank Trollmann; Anni-Yasmin Turhan. Vol. 11117 Cham: Springer International Publishing, 2018. p. 38-45 (Lecture Notes in Computer Science; Vol. 11117).

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

TY - GEN

T1 - Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

AU - Gehrke, Marcel

AU - Braun, Tanya

AU - Möller, Ralf

N1 - Conference code: 218679

PY - 2018/8/30

Y1 - 2018/8/30

N2 - The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently 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. Unfortunately, a non-ideal elimination order can lead to unnecessary groundings.

AB - The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently 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. Unfortunately, a non-ideal elimination order can lead to unnecessary groundings.

UR - https://www.scopus.com/pages/publications/85054535250

U2 - 10.1007/978-3-030-00111-7_4

DO - 10.1007/978-3-030-00111-7_4

M3 - Conference contribution

SN - 978-3-030-00110-0

VL - 11117

T3 - Lecture Notes in Computer Science

SP - 38

EP - 45

BT - KI 2018: Advances in Artificial Intelligence

A2 - Trollmann, Frank

A2 - Turhan, Anni-Yasmin

PB - Springer International Publishing

CY - Cham

T2 - 41st German Conference on Artificial Intelligence<br/>

Y2 - 24 September 2018 through 28 September 2018

ER -

Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

Abstract

DFG Research Classification Scheme

UN SDGs

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