Sciweavers

Share
ECAI
2010
Springer

Learning When to Use Lazy Learning in Constraint Solving

9 years 7 months ago
Learning When to Use Lazy Learning in Constraint Solving
Abstract. Learning in the context of constraint solving is a technique by which previously unknown constraints are uncovered during search and used to speed up subsequent search. Recently, lazy learning, similar to a successful idea from satisfiability modulo theories solvers, has been shown to be an effective means of incorporating constraint learning into a solver. Although a powerful technique to reduce search in some circumstances, lazy learning introduces a substantial overhead, which can outweigh its benefits. Hence, it is desirable to know beforehand whether or not it is expected to be useful. We approach this problem using machine learning (ML). We show that, in the context of a large benchmark set, standard ML approaches can be used to learn a simple, cheap classifier which performs well in identifying instances on which lazy learning should or should not be used. Furthermore, we demonstrate significant performance improvements of a system using our classifier and the lazy lea...
Ian P. Gent, Christopher Jefferson, Lars Kotthoff,
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where ECAI
Authors Ian P. Gent, Christopher Jefferson, Lars Kotthoff, Ian Miguel, Neil C. A. Moore, Peter Nightingale, Karen E. Petrie
Comments (0)
books