AND/OR Tree Search for Constraint Optimization

The paper presents and evaluates the power of a new framework for constraint optimization, based on the concept of AND/OR search trees. The virtue of the AND/OR search tree representation is that its size may be smaller than that of a traditional OR search tree. We introduce a new generation of depth first Branch-and-Bound algorithms that traverse an AND/OR search space and use the Mini-Bucket approximation scheme to generate heuristics to guide the search. Our preliminary experimental work shows that the new approach is competitive and in many cases superior to state of the art systematic search algorithms that explore the regular OR space.