Evaluating and Improving Modern Variable and Revision Ordering
  Strategies in CSPs
  A key factor that can dramatically reduce the search space during constraint
solving is the criterion under which the variable to be instantiated next is
selected. For this purpose numerous heuristics have been proposed. Some of the
best of such heuristics exploit information about failures gathered throughout
search and recorded in the form of constraint weights, while others measure the
importance of variable assignments in reducing the search space. In this work
we experimentally evaluate the most recent and powerful variable ordering
heuristics, and new variants of them, over a wide range of benchmarks. Results
demonstrate that heuristics based on failures are in general more efficient.
Based on this, we then derive new revision ordering heuristics that exploit
recorded failures to efficiently order the propagation list when arc
consistency is maintained during search. Interestingly, in addition to reducing
the number of constraint checks and list operations, these heuristics are also
able to cut down the size of the explored search tree.
