Mini-Bucket Heuristics for Improved Search

Abstract

The paper is a second in a series of two papers evaluating the power of a new scheme that generates search heuristics mechanically. The heuristics are extracted from an approximation scheme called mini-bucket elimination that was recently introduced. The first paper introduced the idea and evaluated it within Branch-and-Bound search. In the current paper the idea is further extended and evaluated within Best-First search. The resulting algorithms are compared on coding and medical diagnosis problems, using varying strength of the mini-bucket heuristics. Our results demonstrate an effective search scheme that permits controlled tradeoff between preprocessing (for heuristic generation) and search. Best-first search is shown to outperform Branch-and-Bound, when supplied with good heuristics, and sufficient memory space.

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

References in corpus (3)

• A Scheme for Approximating Probabilistic Inference
• A New Algorithm for Finding MAP Assignments to Belief Networks
• Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding

Cited by in corpus (1)

• Approximation by Quantization