> From: Don Dailey <[EMAIL PROTECTED]>
>
> I've always had this idea that the best way to build a book might also
> be the best way to build a game playing program. For instance we have
> done these big studies to determine based on games of Leela and others
> what the best main line of play is. Computer Chess programs analyze
> huge databases of games and make tree's sometimes to build opening
> books.
>
> But it seems like this resembles to a remarkable degree a Monte Carlo
> Tree Search program.
Yes, there are analogies. The databases of games in Chess include many
high-quality grandmaster-level games, do they not? I hope that Go databases
also sample professional Dan-level games, otherwise we're just diving into a
pool of ignorance and drawing up a sample.
Joseki databases are "brittle" in a sense. Playing the "almost right" move is
often a failure. I was just reading a particular line in "38 Basic Joseki",
where the author commented that if White plays A in a certain diagram, Black
should reply with B; a difficult semeai follows, which Black wins by one move
if he plays correctly. The result is a gain for Black. On the other hand, if
Black does not know how to play that dificult semeai, Black should not start
down that path at all; it will be a gain for White.
Imagine a program analyzing such a position. Is the program smart enough to
figure out the correct way for Black to win the semeai? If so, the program will
evaluate White A as a dubious move, and avoid it; if playing Black, will
respond correctly and win the battle - and probably the game. But if the
program is not smart enough to play the semeai correctly, it will believe that
White A is a good move.
That's what I mean when I say that evaluation can be brittle. Playing a semeai
properly might mean evaluating the exact min-max (local) outcome of a chain of
20 moves, assuming best play by both sides. Any mis-step will lose the battle.
Any mistake will lead to an evaluation which is off not by a fraction of a
percent, but off by nearly 50% - the difference between balancing at the edge,
50% win/loss, and losing abjectly. Many joseki are that finely balanced - make
the wrong move and your position collapses. Fail to follow up on your
advantage, and your opponent gains at your expense.
In short, I'd say that including joseki and fuseki databases is a Good Thing,
but they must be as complete as possible, and integrated properly into the move
evaluation function. Traps, spoilers, and blunders should be part of the
knowledge base, signposts for the unwary to avoid deep pits.
There are many well-known trick plays which are actually unsound against
correct play, but gain an advantage against weaker players. Imagine the "fool's
mate" multiplied hundreds of times; the field is liberally sown with such trick
plays. Eradicating such blunders will require qualitative analysis; statistical
comparisons of one fool's winrate against another won't help much.
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