Using machine learning to make constraint solver implementation
  decisions
  Programs to solve so-called constraint problems are complex pieces of
software which require many design decisions to be made more or less
arbitrarily by the implementer. These decisions affect the performance of the
finished solver significantly. Once a design decision has been made, it cannot
easily be reversed, although a different decision may be more appropriate for a
particular problem.
  We investigate using machine learning to make these decisions automatically
depending on the problem to solve with the alldifferent constraint as an
example. Our system is capable of making non-trivial, multi-level decisions
that improve over always making a default choice.
