Towards Leveraging Backdoors in Qualitative Constraint Networks

Michael Sioutis; Tomi Janhunen

doi:10.1007/978-3-030-30179-8_27

Towards Leveraging Backdoors in Qualitative Constraint Networks

Michael Sioutis^*, Tomi Janhunen

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	KI 2019
Subtitle of host publication	Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings
Editors	Christoph Benzmüller, Heiner Stuckenschmidt
Pages	308-315
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-030-30179-8_27
Publication status	Published - 1 Jan 2019
MoE publication type	A4 Article in a conference publication
Event	German Conference on Artificial Intelligence - Kassel, Germany Duration: 23 Sep 2019 → 26 Sep 2019 Conference number: 42

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	11793 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	German Conference on Artificial Intelligence
Abbreviated title	KI
Country	Germany
City	Kassel
Period	23/09/2019 → 26/09/2019

Keywords

Backbones
Backdoors
Local consistencies
Qualitative constraints
Spatio-temporal reasoning

Access to Document

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

Fingerprint Dive into the research topics of 'Towards Leveraging Backdoors in Qualitative Constraint Networks'. Together they form a unique fingerprint.

Cite this

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

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)).

@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 = jan,

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",

note = "German Conference on Artificial Intelligence, KI ; Conference date: 23-09-2019 Through 26-09-2019",

}

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

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 proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Towards Leveraging Backdoors in Qualitative Constraint Networks

AU - Sioutis, Michael

AU - Janhunen, Tomi

N1 - Conference code: 42

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

UR - http://www.scopus.com/inward/record.url?scp=85072862958&partnerID=8YFLogxK

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

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

M3 - Conference contribution

AN - SCOPUS:85072862958

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

T2 - German Conference on Artificial Intelligence

Y2 - 23 September 2019 through 26 September 2019

ER -

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