Mini-Bucket Heuristics for Improved Search
Kalev Kask, Rina Dechter

TL;DR
This paper extends the use of mini-bucket heuristics for search algorithms, demonstrating improved performance in best-first search over branch-and-bound in coding and medical diagnosis problems.
Contribution
It introduces an extension of mini-bucket heuristics evaluation within best-first search, showing how to balance preprocessing and search efficiency.
Findings
Best-first search outperforms branch-and-bound with good heuristics
Mini-bucket heuristics enable controlled tradeoff between preprocessing and search
Effective search scheme demonstrated on coding and medical diagnosis problems
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning and Algorithms · AI-based Problem Solving and Planning · Bayesian Modeling and Causal Inference
