Artificial Intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Constraint satisfaction algorithms
Computational Intelligence
Interchangeability preprocessing can improve forward checking search
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
How to solve the Zebra problem, or path consistency the easy way
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Journal of the ACM (JACM)
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Backtrack programming techniques
Communications of the ACM
Exploiting symmetry in the planning graph via explanation-guided search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Ramp activity expert system for scheduling and co-ordination at an airport
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
The effect of Nogood recording in DPLL-CBJ SAT algorithms
ERCIM'02/CologNet'02 Proceedings of the 2002 Joint ERCIM/CologNet international conference on Constraint solving and constraint logic programming
Neighborhood inverse consistency preprocessing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
An empirical study of learning and forgetting constraints
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Building Constraint Satisfaction Problem Solvers Using Rewrite Rules and Strategies
Fundamenta Informaticae
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This paper presents an improved backjumping algorithm for the constraint satisfaction problem, namely conflict-directed backjumping (CBJ). CBJ is then modified such that it can detect infeasible values and removes them from the domains of variables once and for all. A similar modification is then made to Gaschnig's backjumping routine BJ and to Haralick and Elliott's forward checking routine FC. Empirical analysis shows that these modifications tend to result in an improvement in average performance. The existence of a peculiar phenomenon is then shown: the removal of infeasible values may result in a degradation in the performance of intelligent backjumping algorithms, and conversely the addition of infeasible values may lead to an improvement in performance.