C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Representation Selection for Constraint Satisfaction: A Case Study Using n-Queens
IEEE Expert: Intelligent Systems and Their Applications
Constraint Programming Lessons Learned from Crossword Puzzles
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Algorithm selection for sorting and probabilistic inference: a machine learning-based approach
Low-knowledge algorithm control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Switching among Non-Weighting, Clause Weighting, and Variable Weighting in Local Search for SAT
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Empirical hardness models: Methodology and a case study on combinatorial auctions
Journal of the ACM (JACM)
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
A constraint satisfaction framework for executing perceptions and actions in diagrammatic reasoning
Journal of Artificial Intelligence Research
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
An evaluation of machine learning in algorithm selection for search problems
AI Communications - The Symposium on Combinatorial Search
Evaluating component solver contributions to portfolio-based algorithm selectors
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Predicting good propagation methods for constraint satisfaction
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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Constraint programming is a powerful paradigm that offers many different strategies for solving problems. Choosing a good strategy is difficult; choosing a poor strategy wastes resources and may result in a problem going unsolved. We show how Case-Based Reasoning can be used to select good strategies. We design experiments which demonstrate that, on two problems with quite different characteristics, CBR can outperform four other strategy selection techniques.