Easy problems are sometimes hard
Artificial Intelligence
Constraint-based reasoning
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Forward Checking with Backmarking
Constraint Processing, Selected Papers
Journal of Artificial Intelligence Research
The Gn,mphase transition is not hard for the Hamiltonian cycle problem
Journal of Artificial Intelligence Research
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Summarizing CSP hardness with continuous probability distributions
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Randomised restarted search in ILP
Machine Learning
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Recently there has been significant progress in our understanding of the computational nature of combinatorial problems. Randomized search methods, both complete and incomplete, often outperform deterministic strategies. In this paper, we relate the performance of randomized methods to geometric properties of the underlying search space. In particular, our study reveals the inherent fractal nature of the search space, at different length scales, for a range of combinatorial problems. We also discuss the impact of these results on the design of better search methods.