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

^*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	KI 2019
Alaotsikko	Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings
Toimittajat	Christoph Benzmüller, Heiner Stuckenschmidt
Sivut	308-315
Sivumäärä	8
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-030-30179-8_27
Tila	Julkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	German Conference on Artificial Intelligence - Kassel, Saksa Kesto: 23 syyskuuta 2019 → 26 syyskuuta 2019 Konferenssinumero: 42

Julkaisusarja

Nimi	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Kustantaja	Springer
Vuosikerta	11793 LNAI
ISSN (painettu)	0302-9743
ISSN (elektroninen)	1611-3349

Conference

Conference	German Conference on Artificial Intelligence
Lyhennettä	KI
Maa	Saksa
Kaupunki	Kassel
Ajanjakso	23/09/2019 → 26/09/2019

Pääsy asiakirjaan

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

Sormenjälki Sukella tutkimusaiheisiin 'Towards Leveraging Backdoors in Qualitative Constraint Networks'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä

Sioutis, M., & Janhunen, T. (2019). Towards Leveraging Backdoors in Qualitative Constraint Networks. teoksessa C. Benzmüller, & H. Stuckenschmidt (Toimittajat), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings (Sivut 308-315). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 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. Toimittaja / Christoph Benzmüller ; Heiner Stuckenschmidt. 2019. Sivut 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. julkaisussa C Benzmüller & H Stuckenschmidt (toim), 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), Vuosikerta 11793 LNAI, Sivut 308-315, German Conference on Artificial Intelligence, Kassel, Saksa, 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. toim. / Christoph Benzmüller; Heiner Stuckenschmidt. 2019. s. 308-315 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11793 LNAI).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

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. julkaisussa Benzmüller C, Stuckenschmidt H, toimittajat, KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. 2019. s. 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