Constraint satisfaction algorithms
Computational Intelligence
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A fast algorithm for the bound consistency of alldiff constraints
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Heuristics for Dynamically Adapting Propagation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Hi-index | 0.00 |
Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it can be difficult to determine a priori which solving method is best suited to a problem. We explore the use of machine learning to predict which solving method will be most effective for a given problem. Our investigation studies the problem of attribute selection for CSPs, and supervised learning to classify CSP instances drawn from four distinct CSP classes. We limit our study to the choice of two well-known, but simple, CSP solvers. We show that the average performance of the resulting solver is very close to the average performance of a CSP solver based on an oracle.