CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
Experimental studies of variable selection strategies based on constraint weights
Journal of Algorithms
A Study of SAT-Based Branching Heuristics for the CSP
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Learning How to Propagate Using Random Probing
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Heuristics for Dynamically Adapting Propagation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Adaptive Branching for Constraint Satisfaction Problems
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Fast forward planning by guided enforced hill climbing
Engineering Applications of Artificial Intelligence
Evaluating and Improving Modern Variable and Revision Ordering Strategies in CSPs
Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Conflict directed variable selection strategies for constraint satisfaction problems
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Counting-based search: branching heuristics for constraint satisfaction problems
Journal of Artificial Intelligence Research
A hybrid paradigm for adaptive parallel search
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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An important class of CSP heuristics work by sampling information during search in order to inform subsequent decisions. An example is the use of failures, in the form of constraint weights, to guide variable selection in a weighted degree procedure. The present research analyses the characteristics of the sampling process in this procedure and the manner in which information is used, in order to better understand this type of strategy and to discover further enhancements.