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

Institut für Informationssysteme

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.

Originalsprache	Englisch
Titel	KI 2018: Advances in Artificial Intelligence
Redakteure/-innen	Frank Trollmann, Anni-Yasmin Turhan
Seitenumfang	8
Band	11117
Erscheinungsort	Cham
Herausgeber (Verlag)	Springer International Publishing
Erscheinungsdatum	30.08.2018
Seiten	38-45
ISBN (Print)	978-3-030-00110-0
ISBN (elektronisch)	978-3-030-00111-7
DOIs	https://doi.org/10.1007/978-3-030-00111-7_4
Publikationsstatus	Veröffentlicht - 30.08.2018
Veranstaltung	41st German Conference on Artificial Intelligence - Berlin, Deutschland Dauer: 24.09.2018 → 28.09.2018 Konferenznummer: 218679

DFG-Fachsystematik

4.43-01 Theoretische Informatik

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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 (Hrsg.), KI 2018: Advances in Artificial Intelligence (Band 11117, S. 38-45). (Lecture Notes in Computer Science; Band 11117). Springer International Publishing. https://doi.org/10.1007/978-3-030-00111-7_4

@inproceedings{df105afe8a0b4e98be423667b4cf4102,

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 (Hrsg.), KI 2018: Advances in Artificial Intelligence. Bd. 11117, Lecture Notes in Computer Science, Bd. 11117, Springer International Publishing, Cham, S. 38-45, 41st German Conference on Artificial Intelligence
, Berlin, Berlin, Deutschland, 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. Hrsg. / Frank Trollmann; Anni-Yasmin Turhan. Band 11117 Cham: Springer International Publishing, 2018. S. 38-45 (Lecture Notes in Computer Science; Band 11117).

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

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/record/display.uri?eid=2-s2.0-85054535250&origin=inward&txGid=be91c49e9f6ec10e030022c2a524135c

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-Fachsystematik

UN SDGs

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