Towards Leveraging Backdoors in Qualitative Constraint Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Towards Leveraging Backdoors in Qualitative Constraint Networks. / Sioutis, Michael; Janhunen, Tomi.

KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. ed. / Christoph Benzmüller; Heiner Stuckenschmidt. 2019. p. 308-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11793 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Sioutis, M & Janhunen, T 2019, Towards Leveraging Backdoors in Qualitative Constraint Networks. in C Benzmüller & H Stuckenschmidt (eds), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11793 LNAI, pp. 308-315, German Conference on Artificial Intelligence, Kassel, Germany, 23/09/2019. https://doi.org/10.1007/978-3-030-30179-8_27

APA

Sioutis, M., & Janhunen, T. (2019). Towards Leveraging Backdoors in Qualitative Constraint Networks. In C. Benzmüller, & H. Stuckenschmidt (Eds.), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings (pp. 308-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11793 LNAI). https://doi.org/10.1007/978-3-030-30179-8_27

Vancouver

Sioutis M, Janhunen T. Towards Leveraging Backdoors in Qualitative Constraint Networks. In Benzmüller C, Stuckenschmidt H, editors, KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. 2019. p. 308-315. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-30179-8_27

Author

Sioutis, Michael ; Janhunen, Tomi. / Towards Leveraging Backdoors in Qualitative Constraint Networks. KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. editor / Christoph Benzmüller ; Heiner Stuckenschmidt. 2019. pp. 308-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex - Download

@inproceedings{f317493a024746fa8dbec68f4c05c07d,
title = "Towards Leveraging Backdoors in Qualitative Constraint Networks",
abstract = "In this short paper we introduce the notions of backbones and backdoors in the context of qualitative constraint networks. As motivation for the study of those structures, we argue that they can be used to define collaborative approaches among SAT, CP, and native tools, inspire novel decomposition and parallelization techniques, and lead to the development of adaptive constraint propagators with a better insight into the particularities of real-world datasets than what is possible today.",
keywords = "Backbones, Backdoors, Local consistencies, Qualitative constraints, Spatio-temporal reasoning",
author = "Michael Sioutis and Tomi Janhunen",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-30179-8_27",
language = "English",
isbn = "9783030301781",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "308--315",
editor = "Christoph Benzm{\"u}ller and Heiner Stuckenschmidt",
booktitle = "KI 2019",

}

RIS - Download

TY - GEN

T1 - Towards Leveraging Backdoors in Qualitative Constraint Networks

AU - Sioutis, Michael

AU - Janhunen, Tomi

PY - 2019/1/1

Y1 - 2019/1/1

N2 - In this short paper we introduce the notions of backbones and backdoors in the context of qualitative constraint networks. As motivation for the study of those structures, we argue that they can be used to define collaborative approaches among SAT, CP, and native tools, inspire novel decomposition and parallelization techniques, and lead to the development of adaptive constraint propagators with a better insight into the particularities of real-world datasets than what is possible today.

AB - In this short paper we introduce the notions of backbones and backdoors in the context of qualitative constraint networks. As motivation for the study of those structures, we argue that they can be used to define collaborative approaches among SAT, CP, and native tools, inspire novel decomposition and parallelization techniques, and lead to the development of adaptive constraint propagators with a better insight into the particularities of real-world datasets than what is possible today.

KW - Backbones

KW - Backdoors

KW - Local consistencies

KW - Qualitative constraints

KW - Spatio-temporal reasoning

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U2 - 10.1007/978-3-030-30179-8_27

DO - 10.1007/978-3-030-30179-8_27

M3 - Conference contribution

SN - 9783030301781

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 308

EP - 315

BT - KI 2019

A2 - Benzmüller, Christoph

A2 - Stuckenschmidt, Heiner

ER -

ID: 37822652