Proceedings of the AAAI Conference on Artificial Intelligence, 21
Uncertainty in AI
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. AND/OR Branch-and-Bound (AOBB) is a new algorithm that explores the AND/OR search tree for solving optimization tasks in graphical models. In this paper we extend the algorithm to explore an AND/OR search graph by equipping it with a context-based adaptive caching scheme similar to good and no-good recording. The efficiency of the new graph search algorithm is demonstrated empirically on various benchmarks, including the very challenging ones that arise in genetic linkage analysis.