On 9x9, playing against people, the opening learning is very important so
that they don’t find a winning sequence then just repeat it.  This has been
in Igowin since it first shipped, in 1998.  I don’t think it helps the
strength much in 19x19, but it makes the games look prettier.

I use a persistent position hash table that stores every position visited
twice (or more) by games that were deliberately added (File, Add Game(s) to
database), and every position in every game played locally.  The program
ships with a 19x19 database built from about 40K games.  The next update
will also include 9x9 games from strong cgos programs.

Move choices are biased based on the win/loss ratio, number of visits,
strength of strongest player moving into that position, depth in the game,
and probably more that I have long since forgotten.  There is no hard
pruning or immediate move choices from this data, just biasing the search.

David

> -----Original Message-----
> From: [email protected] [mailto:computer-go-
> [email protected]] On Behalf Of Petr Baudis
> Sent: Thursday, October 14, 2010 3:02 AM
> To: [email protected]
> Subject: [Computer-go] ManyFaces learning
> 
> On Wed, Oct 13, 2010 at 11:47:01PM -0700, David Fotland wrote:
> > > > Many Faces learns.  I think most everyone follows the policy.  As a
> > > > developer, I want each version of my program to have a separate name
> so
> > > Learns in what way?  Won't repeat the exact same loss twice?
> > Yes.
> 
> Intriguing! Do you think it actually improves its strength, or is that
> just an experiment?
> 
> How does it know where the "losing variation" starts? Is it updating
> some pattern weights, or updating some persistent game tree?
> 
> Thanks,
> 
> --
>                               Petr "Pasky" Baudis
> The true meaning of life is to plant a tree under whose shade
> you will never sit.
> _______________________________________________
> Computer-go mailing list
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