TY - CHAP

T1 - On tightness of constraints

AU - Zhang, Yuanlin

PY - 2004

Y1 - 2004

N2 - The tightness of a constraint refers to how restricted the constraint is. The existing work shows that there exists a relationship between tightness and global consistency of a constraint network. In this paper, we conduct a comprehensive study on this relationship. Under the concept of k-consistency (k is a number), we strengthen the existing results by establishing that only some of the tightest, not all, binary constraints are used to predict a number k such that strong k-consistency ensures global consistency of an arbitrary constraint network which may include non-binary constraints. More importantly, we have identified a lower bound of the number of the tightest constraints we have to consider in predicting the number k. To make better use of the tightness of constraints, we propose a new type of consistency: dually adaptive consistency. Under this concept, only the tightest directionally relevant constraint on each variable (and thus in total n - 1 such constraints where n is the number of variables) will be used to predict the level of "consistency" ensuring global consistency of a network.

AB - The tightness of a constraint refers to how restricted the constraint is. The existing work shows that there exists a relationship between tightness and global consistency of a constraint network. In this paper, we conduct a comprehensive study on this relationship. Under the concept of k-consistency (k is a number), we strengthen the existing results by establishing that only some of the tightest, not all, binary constraints are used to predict a number k such that strong k-consistency ensures global consistency of an arbitrary constraint network which may include non-binary constraints. More importantly, we have identified a lower bound of the number of the tightest constraints we have to consider in predicting the number k. To make better use of the tightness of constraints, we propose a new type of consistency: dually adaptive consistency. Under this concept, only the tightest directionally relevant constraint on each variable (and thus in total n - 1 such constraints where n is the number of variables) will be used to predict the level of "consistency" ensuring global consistency of a network.

UR - http://www.scopus.com/inward/record.url?scp=35048878333&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-30201-8_65

DO - 10.1007/978-3-540-30201-8_65

M3 - Chapter

AN - SCOPUS:35048878333

SN - 3540232419

SN - 9783540232414

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

SP - 777

EP - 781

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

A2 - Wallace, Mark

PB - Springer-Verlag

ER -