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About the choice of the variable to unassign in a decision repair algorithm

Published online by Cambridge University Press:  15 July 2005

Cédric Pralet  and
Gérard Verfaillie
Cédric Pralet
Affiliation:
LAAS-CNRS, Toulouse, France; cpralet@laas.fr; gverfail@laas.fr
Gérard Verfaillie
Affiliation:
LAAS-CNRS, Toulouse, France; cpralet@laas.fr; gverfail@laas.fr
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Abstract

The decision repair algorithm (Jussien and Lhomme, Artificial Intelligence139 (2002) 21–45), which has been designed to solve constraint satisfaction problems (CSP), can be seen, either (i) as an extension of the classical depth first tree search algorithm with the introduction of a free choice of the variable to which to backtrack in case of inconsistency, or (ii) as a local search algorithm in the space of the partial consistent variable assignments. or (iii) as a hybridisation between local search and constraint propagation. Experiments reported in Pralet and Verfailllie (2004) show that some heuristics for the choice of the variable to which to backtrack behave well on consistent instances and that other heuristics behave well on inconsistent ones. They show also that, despite its a priori incompleteness, decision repair, equipped with some specific heuristics, can solve within a limited time almost all the consistent and inconsistent randomly generated instances over the whole constrainedness spectrum. In this paper, we discuss the heuristics that could be used by decision repair to solve consistent instances, on the one hand, and inconsistent ones, on the other hand. Moreover, we establish that some specific heuristics make decision repair complete.

Keywords

Constraint satisfaction problemdepth first tree searchlocal searchconstraint propagationbacktrackheuristicscompleteness.
Type
Research Article
Information
RAIRO - Operations Research , Volume 39 , Issue 1 , January 2005 , pp. 55 - 74
Copyright
© EDP Sciences, 2005

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References

E. Aarts and J. Lenstra, Eds. Local Search in Combinatorial Optimization. John Wiley & Sons (1997).
C. Bessière and J.C. Régin, MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?), in Proc. of the 2nd International Conference on Principles and Practice of Constraint Programming (CP-96)). Cambridge, MA, USA. Lect. Notes Comput. Sci. (1996) 61–75.
C. Bliek, Generalizing Partial Order and Dynamic Backtracking, in Proc. of the 15th National Conference on Artificial Intelligence (AAAI-98). Madison, WI, USA (1998) 319–325.
R. Dechter and R. Mateescu, Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search Space, in Proc. of the 20th International Conference on Uncertainty in Artificial Intelligence (UAI-04). Banff, Canada (2004).
Ginsberg, M., Dynamic Backtracking. J. Artif. Intell. Res. 1 (1993) 25–46.
M. Ginsberg and D. McAllester, GSAT and Dynamic Backtracking, in Proc. of the 4th International Conference on the Principles of Knowledge Representation and Reasoning (KR-94). Bonn, Germany (1994) 226–237.
C. Gomes and B. Selman, Problem Structure in the Presence of Perturbations, in Proc. of the 14th National Conference on Artificial Intelligence (AAAI-97). Providence, RI, USA (1997).
Jussien, N. and Lhomme, O., Local Search with Constraint Propagation and Conflict-based Heuristics. Artif. Intell. 139 (2002) 21–45. CrossRef
A. Mackworth, Constraint Satisfaction, in Encyclopedia of Artificial Intelligence, S. Shapiro, Ed. John Wiley & Sons (1992) 285–293.
C. Pralet and G. Verfailllie, Travelling in the World of Local Searches in the Space of Partial Assignments, in Proc. of the International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimisation Problems (CP-AI-OR-04). Nice, France (2004) 240–255.
Prestwich, S., Combining the Scalability of Local Search with the Pruning Techniques of Systematic Search. Ann. Oper. Res. 115 (2002) 51–72. CrossRef
Prosser, P., Hybrid Algorithms for the Constraint Satisfaction Problems. Comput. Intell. 9 (1993) 268–299. CrossRef