Abstract
A conditional constraint satisfaction problem (CCSP) extends a standard constraint satisfaction problem (CPS) with a condition-based component that controls what variables participate in problem solutions. CCSPs adequately represent configuration and design problems in which selected subsets of variables, rather than the entire variable set, are relevant to final solutions. The only algorithm that is available for CCSP and operates directly on the original, unreformulated CCSP statement has been basic backtrack search. Reformulating CCSPs into standard CSPs has been proposed in order to bring the full arsenal of CSP algorithms to bear. One reformulation approach adds null values to variable domains and transforms CCSP constraints into CSP constraints. However, a complete null-based reformulation of CCSPs has not been available. In this paper we provide more advanced algorithms for CCSP and a full null-based reformulation into standard CSP. Thorough testing reveals that the advanced algorithms perform up to two orders of magnitude better than plain backtracking, but that realizing practical advantages from reformulation is problematic. The advanced algorithms extend forward checking and maintaining arc consistency to CCSPs. The null-based reformulation improves on the preliminary findings in [1] by removing the limitation on multiple activation, and by localizing changes. It identifies and addresses a difficulty presented by activity cycles.
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
Gelle, E.: On the generation of locally consistent solution spaces. Ph.D. Thesis, Ecole Polytechnique Fédérale de Lausanne, Switzerland (1998)
Mittal, S., Falkenhainer, B.: Dynamic constraint satisfaction problems. In: Proceedings of the Eighth National Conference on Artificial Intelligence (1990)
Sabin, M., Freuder, E.: Detecting and resolving inconsistency and redundancy in conditional constraint satisfaction problems. In: Web-published papers of the CP 1998 Workshop on Constraint Problem Reformulation, Pisa, Italy (1998)
Dechter, R., Dechter, A.: Belief maintenance in dynamic constraint networks. In: Proceedings of AAAI 1988, pp. 37–42 (1988)
Bessière, C.: Arc-consistency in dynamic constraint satisfaction problems. In: Proceedings of the 9th AAAI, pp. 221–226 (1991)
Verfaillie, G., Schiex, T.: Solution reuse in dynamic constraint satisfaction problems. In: Proceedings of the 12th AAAI, Seattle, WA, pp. 307–312 (1994)
Sabin, D., Sabin, M., Russell, R., Freuder, E.: A constraint-based approach to diagnosing software problems in computer networks. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976. Springer, Heidelberg (1995)
Sabin, M., Russell, R., Miftode, I.: Using constraint technology to diagnose errors in networks managed with spectrum. In: Proceedings of the IEEE Internationl Conference on Telecommunications, Bucharest, Romania (2001)
Soininen, T., Gelle, E., Niemelä, I.: A fixpoint definition for dynamic constraint satisfaction. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 419–434. Springer, Heidelberg (1999)
Gelle, E., Faltings, B.: Solving mixed and conditional constraint satisfaction problems. Constraints 8, 107–141 (2003)
Wallace, R.: Random CSP Generator. Constraint Computation Center, University of New Hampshire, Durham, NH, U.S.A (1996), http://www.cs.unh.edu/ccc/code.html
Sabin, M.: Solving and Reformulation of Conditional Constraint Satisfaction Problems. PhD thesis, University of New Hampshire, Durham, NH, U.S.A (2003)
Haselböck, A.: Knowledge-based Configuration and Advanced Constraint Technologies. PhD thesis, Technical University of Vienna (1993)
Haralick, R., Elliott, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–313 (1980)
Mackworth, A.: Consistency in networks of relations. Artificial Intelligence 8 (1977)
Sabin, D., Freuder, E.: Contradicting conventional wisdom in constraint satisfaction. In: Borning, A. (ed.) PPCP 1994. LNCS, vol. 874. Springer, Heidelberg (1994)
Grant, S., Smith, B.: The phase transition behavior of maintaining arc consistency. Technical Report 92.95, School of Computing, University of Leeds (A revised and shortened version appears in Proceedings ECAI 1996, pp. 175-179 (1996s)
Bessière, C., Régin, J.C.: MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 61–75. Springer, Heidelberg (1996)
Bessière, C., Régin, J.C.: Arc consistency for general constraint networks: preliminary results. In: Proceedings IJCAI 1997, Nagoya, Japan, pp. 398–404 (1997)
Mohr, R., Henderson, T.: Arc and path consistency revisited. Aritificial Intelligence 28, 225–233 (1986)
Freuder, E., Wallace, R.: Partial constraint satisfaction. Artificial Intelligence 58 (1992)
Bessière, C., Régin, J.C.: Refining the basic constraint propagation algorithm. In: Proceedings IJCAI 2001, Seattle, WA, pp. 309–315 (2001)
Bessière, C., Meseguer, P., Freuder, E., Larrosa, J.: On forward checking for nonbinary constraint satisfaction. Artificial Intelligence 141, 205–224 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sabin, M., Freuder, E.C., Wallace, R.J. (2003). Greater Efficiency for Conditional Constraint Satisfaction. In: Rossi, F. (eds) Principles and Practice of Constraint Programming – CP 2003. CP 2003. Lecture Notes in Computer Science, vol 2833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45193-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-45193-8_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20202-8
Online ISBN: 978-3-540-45193-8
eBook Packages: Springer Book Archive