I think he is missing the tree search part.  Just doing a one-ply
lookahead and then doing playouts will not make a strong bot.  I would
like to defer an explanation of UCT (or something else) to someone who
is more of an expert.

- George

On Mon, Jul 6, 2009 at 8:25 PM, Raymond Wold<computergol...@w-wins.com> wrote:
> Fred Hapgood wrote:
>>
>> I have a really basic question about how MC works in the context of Go.
>>
>> Suppose the problem is to make the first move in a game, and suppose we
>> have accepted as a constraint that we will abstain from just copying
>> some joseki out of a book -- we are going to use MC to figure out the
>> first move de novo. We turn on the software and it begins to play out
>> games. My question is: how does the software pick its first move?  Does
>> it move entirely at random? Sometimes it sounds that way MC works is by
>> picking each move at random, from the first to the last, for a million
>> games or so. The trouble is that the number of possible Go games is so
>> large that a million games would not even begin to explore the
>> possibilities.  It is hard to imagine anything useful emerging from
>> examining such a small number. So I'm guessing that the moves are not
>> chosen at random.  But even if you reduce the possibilities to two
>> options per move, which would be pretty impressive, you'd still run out
>> of your million games in only twenty moves, after which you would be
>> back to picking at random again.
>
> We don't know why it works. It's just a matter of empirical fact that the
> win rate in random play-outs is a decent indicator of the strength of a
> move. The math involved is likely to be hideous and probably of little
> practical interest, and I don't know of anyone that has tried.
>
>> What am I missing??
>
> I don't think you're missing anything at all.
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