The system is described together with the results of running the system on several different types of problems. Bayesian Learning, discovery, explanation-based learning, minimum description length. Critical to this process is identifying uncertainty about the ultimate value of any move. The lower bound on uncertainty is the best estimate of the real value of a move. The upper bound is its optimistic value, based on some measure of unexplored potential.
Uncertainty is represented by probability distributions.
The search develops those parts of the tree where moving existing bounds would be most likely to succeed and would make the most progress toward terminating the search. Termination is achieved when the established real value of the best move is so good that the likelihood of this being achieved by any other alternative is minimal. This report describes the implementation, and the results of tests including games played against brute-force programs. It exhibits significant speed up over a serial version of the same algorithm on a SUN-4. The implementation is described together with some performance comparisons with the serial version.