Abstract
AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. Since the variable selection can have a dramatic impact on search performance when solving optimization tasks, we introduce in this paper a new dynamic AND/OR Branchand- Bound algorithmic framework which accommodates variable ordering heuristics. The efficiency of the dynamic AND/OR approach is demonstrated empirically in a variety of domains.