The old, knowledge based programs all used large pattern databases.
Performance of the pattern matcher was never an issue.  I think it was about
3% of the time in Many Faces.  If this is going to work you will need some
breakthrough in choosing and evaluating the patterns to use.

David

> -----Original Message-----
> From: [email protected]
[mailto:[email protected]]
> On Behalf Of [email protected]
> Sent: Monday, June 14, 2010 8:02 AM
> To: [email protected]
> Subject: [Computer-go] re. : Effective search depth
> 
> > So what can above calculation tell us? According to above calculation it
> could estimate
> > that the effective search depth of the today's strong Go programs are
about
> 11, if the playout
> > number is one million and assume m=600, f=1.5. If an effective search
depth
> of 50
> > is required to reach high dan level. Then the playout number needs to
> increase by a
> > factor of 1.5^39, about 7.4 milliom times. That is 7.4 trillion playouts
> needed.
> 
> I suspect that Monte-Carlo isn't the best way to achieve high dan level
play,
> even though
> it is the best Go playing method at the moment.
> I'm doing research into board evaluation using pattern recognition, with
the
> key being an
> algorithm that can find patterns on a board without the time required
being
> proportional
> to the number of patterns being searched for.
> My aim would then be to only do lookahead as far as the next tenuki play,
in
> the sense
> that for every move examined the likely sequence of moves following it
would
> be examined
> up to the point where the next play at each leaf node was a tenuki, and at
> that point the
> evaluation of the board at the leaf node would be used to min-max the tree
of
> moves to
> decide best move. The depth would obviously vary, but the total number of
> moves to
> evaluate would be unlikely to be above a thousand.
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