Abstract
A conditional constraint satisfaction problem (CCSP) is a variant of the standard constraint satisfaction problem (CSP). CCSPs model problems where some of the variables and constraints may be conditionally inactive such that they do not participate in a solution. Recently, algorithms were introduced that use MAC at their core to solve CCSP. We extend MAC with a simple assumption-based reasoning. The resulting algorithm, Activity MAC (AMAC), is able to achieve significantly better pruning than existing methods. AMAC is shown to be more than two orders of magnitude more efficient than CondMAC on certain problem classes. Our algorithm is most naturally expressed using a variant of the CCSP representation that we refer to as Activity CSP (ACSP). ACSP introduces activity variables which explicitly control the presence of other variables in the solution. Common aspects of CCSP, such as activity clustering and disjunction, are easily captured by ACSP and contribute to improved pruning by AMAC.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sabin, M., Freuder, E.C., Wallace, R.J.: Greater efficiency for conditional constraint satisfaction. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 649–663. Springer, Heidelberg (2003)
Gelle, E., Faltings, B.: Solving mixed and conditional constraint satisfaction problems. Constraints 8, 107–141 (2003)
Mittal, S., Falkenhainer, B.: Dynamic constraint satisfaction problems. In: Proc. of AAAI 1990, Boston, MA, pp. 25–32 (1990)
Bin, E., Emek, R., Shurek, G., Ziv, A.: Using a constraint satisfaction formulation and solution techniques for random test program generation. IBM Systems Journal 41, 386–402 (2002)
de Kleer, J.: An assumption-based tms. Artif. Intell. 28, 127–162 (1986)
Soininen, T., Gelle, E., Niemelä, I.: A fixpoint definition of dynamic constraint satisfaction. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 419–434. Springer, Heidelberg (1999)
Mackworth, A.K.: Consistency in networks of relations. Artificial Intelligence 8, 99–118 (1977)
de Kleer, J.: A comparison of atms and csp techniques. In: IJCAI, pp. 290–296 (1989)
McAllester, D.A.: Truth maintenance. In: AAAI, pp. 1109–1116 (1990)
Gomes, C.P., Selman, B., Crato, N., Kautz, H.A.: Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. Journal of Automated Reasoning 24, 67–100 (2000)
Jussien, N., Debruyne, R., Boizumault, P.: Maintaining arc-consistency within dynamic backtracking. In: Principles and Practice of Constraint Programming, pp. 249–261 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Geller, F., Veksler, M. (2005). Assumption-Based Pruning in Conditional CSP. In: van Beek, P. (eds) Principles and Practice of Constraint Programming - CP 2005. CP 2005. Lecture Notes in Computer Science, vol 3709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564751_20
Download citation
DOI: https://doi.org/10.1007/11564751_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29238-8
Online ISBN: 978-3-540-32050-0
eBook Packages: Computer ScienceComputer Science (R0)