I understand that Monte Carlo algorthms are driven by the "winning
probability", and a 0.5 win looks as good - or maybe even better - than a
100-point win.
However, the estimated probability of winning may be way off. It is well known
that Mogo, and perhaps some other programs, fail to recognize common nakade
placements, which leads to fundamental estimation errors. An algorithm with
more of a "fighting spirit" would defend against nakade, and attack enemy
groups; perhaps making up for the loss of one group by the capture of another.
Any algorithm which drives the win toward 0.5 is always going to be brittle;
any problems with the accuracy of the evaluation may place one on the losing
side of that 0.5 divide. Hence, if it is possible to be more greedy - to seal
off groups, to play for every yose point, to fill in dame first - programs
based on monte carlo will improve. It's terrible to lose a won game by drifting
over the edge of a precipice. When monte carlo programs lose, they lose big --
in my so-far limited observations, incorrect evaluation of life-and-death
status leads to estimation errors which far exceed the 0.5 margin of error.
For a large number of playouts, the estimated scores should converge as the
game progresses. This is particularly true if the random distributions strongly
favor moves where each opponent monotonically increases the score - keeping
one's groups alive, keeping the opponent dead, and growing areas wherever
possible. Of course there must be enough variability to permit sacrifice plays
and nakade placements; throwing a stone into a group will initially look like a
bad play, but if the placement succeeds, it is a very good play, the success of
which must be properly attributed to the earlier placement - or even further
back, to the surrounding and cutting and eye-killing moves which ultimately led
to the placement move.
Terry McIntyre <[EMAIL PROTECTED]>
They mean to govern well; but they mean to govern. They promise to be kind
masters; but they mean to be masters. -- Daniel Webster
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