Learning When to Use Lazy Learning in Constraint Solving

Ian Philip Gent, Christopher Anthony Jefferson, Lars Kotthoff, Ian James Miguel, Neil Charles Armour Moore, Peter Nightingale, Karen Petrie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)


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 learning and standard constraint solvers over a standard solver. Through rigorous cross-validation across the different problem classes in our benchmark set, we show the general applicability of our learned classifier.
Original languageEnglish
Title of host publicationProceedings of the 19th European Conference on Artificial Intelligence (ECAI-2010)
Number of pages6
Publication statusPublished - 2010

Publication series

Name Frontiers in Artificial Intelligence and Applications
PublisherIOS Press
ISSN (Print)0922-6389


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