  Effective solving of constraint problems often requires choosing good or
specific search heuristics. However, choosing or designing a good search
heuristic is non-trivial and is often a manual process. In this paper, rather
than manually choosing/designing search heuristics, we propose the use of
bandit-based learning techniques to automatically select search heuristics. Our
approach is online where the solver learns and selects from a set of heuristics
during search. The goal is to obtain automatic search heuristics which give
robust performance. Preliminary experiments show that our adaptive technique is
more robust than the original search heuristics. It can also outperform the
original heuristics.
