The Adaptive Constraint Engine
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Limited assignments: a new cutoff strategy for incomplete depth-first search
Proceedings of the 2005 ACM symposium on Applied computing
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Experimental studies of variable selection strategies based on constraint weights
Journal of Algorithms
Using a Choice Function for Guiding Enumeration in Constraint Solving
MICAI '10 Proceedings of the 2010 Ninth Mexican International Conference on Artificial Intelligence
A hyperheuristic approach for dynamic enumeration strategy selection in constraint satisfaction
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
A framework for autonomous search in the Eclipsesolver
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
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In Constraint Programming, enumeration strategies play an important role, they can significantly impact the performance of the solving process. However, choosing the right strategy is not simple as its behavior is commonly unpredictable. Autonomous search aims at tackling this concern, it proposes to replace bad performing strategies by more promising ones during the resolution. This process yields a combination of enumeration strategies that worked during the search phase. In this paper, we focus on the study of this combination by carefully tracking the resolution. Our preliminary goal is to find good enumeration strategy blends for a given Constraint Satisfaction Problem.