From: Nick Wedd <[EMAIL PROTECTED]>
>>which will alter the score not by one point, but by ten or twenty.
>>Their estimate of winning probability is totally wrong. Good players
>>winnow out losing moves and stick with good moves - the basic premise
>>of minimax searching. Losing a big group will lead to a win only if
one
>>obtains equivalent compensation elsewhere. Good players sometimes make
>>sacrifice plays, but failing to recognize that one's group is lost
will
>>totally skew one's estimate of one's winning chances.
>We all know that MC programs don't play perfectly. What point are you
>making?
I begin with the empirical observation that existing MC programs can be very
wrong in their estimation of the likely score of given positions, and therefore
of the winning probabilities of the various moves. Consider a group which dies
due to a skillful placement. If I know about this irrefutable move, I estimate
that
your group is dead, and the score is 20 points in my favor. If I do not know
that
move, my estimate might be that the game is yours by half a point.
Now, in a universe of equally blind bots, a poor estimate can be better than a
slightly worse estimate; but to compete against pro human players, one must be
able to get fairly close to the correct score for stable positions.
My point is that there appears to be a systemic error in the existing
estimation process.
Relating back to the move generation used by "random" playouts; amongst skilled
players, it is quite common to consider ways to reduce groups to the
single-eyed state.
( The Cotsen Go Tournament sweatshirts read "Cyclops Assassin" for this reason.
)
Under fast time controls, humans may fail to notice some nakade opportunities.
It is
also possible to exploit nakade, but to have a bot continue playing as if it
were still in
the game, eventually winning on time. When human players discover that it is
important
to explicitly kill such groups, the winning rates of such bots will plummet.
I suggest that a useful stage in the evolution of "heavy playouts" might be to
incorporate
provably-correct analysis of group status, placements, and "fighting spirit"
heuristics. This would
skew the distribution of playouts toward that actually used by skilled players,
improving the accuracy
of the estimation. As my friend and coach Chris H often says: "When in doubt,
read it out."
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