Physica D
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Elements of information theory
Elements of information theory
Exploiting the deep structure of constraint problems
Artificial Intelligence
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
New methods to color the vertices of a graph
Communications of the ACM
The Art of Computer Programming Volumes 1-3 Boxed Set
The Art of Computer Programming Volumes 1-3 Boxed Set
Combinatorial Algorithms: For Computers and Hard Calculators
Combinatorial Algorithms: For Computers and Hard Calculators
Graphs and Hypergraphs
A Mathematical Theory of Communication
A Mathematical Theory of Communication
An empirical analysis of search in GSAT
Journal of Artificial Intelligence Research
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Exploiting a theory of phase transitions in three-satisfiability problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
The very particular structure of the very hard instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
An Investigation of Variable Relationships in 3-SAT Problems
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
The Gn,mphase transition is not hard for the Hamiltonian cycle problem
Journal of Artificial Intelligence Research
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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The typical difficulty of various NP-hard problems varies with simple parameters describing their structure. This behavior is largely independent of the search algorithm, but depends on the choice of problem ensemble. A given problem instance belongs to many different ensembles, so applying these observations to individual problems requires identifying which ensemble is most appropriate for predicting its search behavior, e.g., cost or solubility. To address this issue, we introduce a readily computable measure of randomness for search problems called "approximate entropy". This new measure is better suited to search than other approaches, such as algorithmic complexity and information entropy. Experiments with graph coloring and 3-SAT show how this measure can be applied.