  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.

Artificial Intelligence