Abstract
Nowadays, many real problem in Artificial Intelligence can be modeled as constraint satisfaction problems (CSPs). A general rule in constraint satisfaction is to tackle the hardest part of a search problem first. In this paper, we introduce a parameter (τ) that measures the constrainedness of a search problem. This parameter represents the probability of the problem being feasible. A value of τ= 0 corresponds to an over-constrained problem and no states are expected to be solutions. A value of τ=1 corresponds to an under-constrained problem which every state is a solution. This parameter can also be used in a heuristic to guide search. To achieve this parameter, a sample in finite population is carried out to compute the tightnesses of each constraint. We take advantage of this tightnesses to classify the constraints from the tightest constraint to the loosest constraint. This heuristic may accelerate the search due to inconsistencies can be found earlier and the number of constraint checks can significantly be reduced.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bartak, R.: Constraint programming. In: pursuit of the holy grail. In: Proceedings of WDS 1999 (invited lecture), Prague (June 1999)
Dechter, R., Pearl, J.: Network-based heuristics for constraint satisfaction problems. Artificial Intelligence 34, 1–38 (1988)
Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of search. In: Proceedings of AAAI 1996, pp. 246–252 (1996)
Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of arc consistency. Principles and Practice of Constraint Programming, 327–340 (1997)
Haralick, R., Elliot, G.: Increasing tree efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–314 (1980)
Kumar, V.: Algorithms for constraint satisfaction problems: a survey. Artificial Intelligence Magazine 1, 32–44 (1992)
Sadeh, N., Fox, M.S.: Variable and value ordering heuristics for activity-based jobshop scheduling. In: Proc. of Fourth International Conference on Expert Systems in Production and Operations Management, pp. 134–144 (1990)
Salido, M.A., Giret, A., Barber, F.: Distributing Constraints by Sampling in Non-Binary CSPs. In: IJCAI Workshop on Distributing Constraint Reasoning, pp. 79–87 (2003)
Wallace, R., Freuder, E.: Ordering heuristics for arc consistency algorithms. In: Proc. of Ninth Canad. Conf. on A.I., pp. 163–169 (1992)
Walsh, T.: The constrainedness knife-edge. In: Proceedings of the 15th National Conference on AI (AAAI 1998), pp. 406–411 (1998)
Waltz, D.L.: Understanding line drawings of scenes with shadows. The Psychology of Computer Vision, 19–91 (1975)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Salido, M.A., Barber, F. (2004). Exploiting the Constrainedness in Constraint Satisfaction Problems. In: Bussler, C., Fensel, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2004. Lecture Notes in Computer Science(), vol 3192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30106-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-30106-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22959-9
Online ISBN: 978-3-540-30106-6
eBook Packages: Springer Book Archive