Paper Info
Title
A Choice Function to Dynamic Selection of Enumeration Strategies Solving Constraint Satisfaction Problems
Abstract
In this work we exploit search process features to dynamically adapt a Constraint Programming solver in order to more efficiently solve Constraint Satisfaction Problems. The main novelty of our approach is that we reconfigure the searching or search process based solely on performance data gathered while solving the current problem. We report encouraging results where our combination of strategies outperforms the use of individual strategies
Year
DOI
Venue
2009
10.1109/SoCPaR.2009.54
SoCPaR
Keywords
Field
DocType
enumeration strategies,dynamic selection,choice function,constraint programming solver,individual strategy,current problem,main novelty,constraint satisfaction problems,performance data,search process,search process feature,constraint programming,data mining,tuning,programming,constraint satisfaction problem,probability density function,data gathering
Constraint satisfaction,Mathematical optimization,Local consistency,Computer science,Constraint programming,Constraint satisfaction problem,Constraint satisfaction dual problem,Concurrent constraint logic programming,Artificial intelligence,Backtracking,Machine learning,Hybrid algorithm (constraint satisfaction)
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
4
Name
Order
Citations
PageRank
Broderick Crawford144673.74
Mauricio Montecinos200.68
Carlos Castro325529.05
Eric Monfroy457963.05