Erica and many Faces do not set c = 0, so there are many strong go programs 
that do not set c to zero.  Fuego and Mogo use c = 0.  I'm not sure about 
others.

David

> -----Original Message-----
> From: [email protected] [mailto:computer-go-
> [email protected]] On Behalf Of Jason House
> Sent: Tuesday, October 26, 2010 6:01 AM
> To: ???? ??????; [email protected]
> Subject: Re: [Computer-go] Monte Carlo (upper confidence bounds applied to
> trees)
> 
> It looks like your example is using average win rate instead of an upper
> bound. Rather than M/N UCT uses M/N + c*log(N1+N2+N3+...)/sqrt(N)
> 
> where Ni is the total simulations of the ith move. That extra term means
> moves with initially bad results will be revisited. Stronger go programs add
> in some other terms and set c=0, but that isn't UCT.
> 
> Sent from my iPhone
> 
> On Oct 26, 2010, at 6:43 AM, ???? ?????? <[email protected]> wrote:
> 
> > Hello all!
> > I would much appreciate if someone could explain me what exactly "upper
> confidence bounds applied to trees" is.
> >
> > I'm still not sure that UCT works good.
> >
> > For example, I have a position with two possible moves (a1 and b1):
> >
> >    O
> >  /   \
> > (a1) (b1)
> >
> >
> > First random game (using a1 as first move) returns 0, second (b1) returns
> 1:
> >
> >    O
> >  /   \
> > (a1) (b1)
> > (0/1)(1/1)
> >
> >
> > Ok, let's try to explore b1 move (the program likes it, because uct of a1
> = 0):
> >
> >    O
> >  /   \
> > (a1) (b1)
> > (0/1)(1/1)
> >     / | \
> >   a2 b2 c2
> >
> >
> > a2 returns 0, b2 and c2 returns 1:
> >
> >    O
> >  /   \
> > (a1) (b1)
> > (0/1)(3/4)
> >     / | \
> >   a2 b2 c2
> >   0   1  1
> >
> >
> > So the program will explore b2 and c2 (starting from b1), but maybe a2 is
> the only refuting move. Maybe a1 is better, but the program will continue to
> explore b1, because utc of a1 is 0 and utc of b1 is more than 0.
> > _______________________________________________
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