I think I've seen some discussion a while ago about a set of tests
used to test playing engines. I suppose it would make sense to
compile such a set for the reference bots. Matching a win-rate and
game-length after 1,000,000 playouts is a quick first milestone for
verification that can be d
On 30-nov-08, at 16:51, Jason House wrote:
You've claimed to be non-statistical, so I'm hoping the following
is useful... You can compute the likelihood that you made an
improvement as:
erf(# of standard deviations)
Where # of standard deviations =
(win rate - 0.5)/sqrt(#games)
Erf is ill
You can read about some such novelties found using Rybka here:
http://www.rybkachess.com/index.php?auswahl=Rybka+3+book
Don Dailey wrote:
On Sun, 2008-11-30 at 14:33 -0800, terry mcintyre wrote:
- Original Message
From: Don Dailey <[EMAIL PROTECTED]>
MCTS really feels to me like a
On Sun, 2008-11-30 at 14:33 -0800, terry mcintyre wrote:
>
> - Original Message
> > From: Don Dailey <[EMAIL PROTECTED]>
> >
> > MCTS really feels to me like a superb book building algorithm.
> > Computer Chess books (at least the automated part) are built essentially
> > by taking milli
- Original Message
> From: Don Dailey <[EMAIL PROTECTED]>
>
> MCTS really feels to me like a superb book building algorithm.
> Computer Chess books (at least the automated part) are built essentially
> by taking millions of games from master play and picking out the ones
> that seem to
It's true that building an opening book in an automated way can be done
off-line which gives us more resources. That's really the basis for
this thought that we are trying to solve the same problem.
As a thought experiment, imagine some day in the future, when
computers are 1 thousand times
On Sun, 2008-11-30 at 14:49 -0200, Mark Boon wrote:
> Indeed, the scaling question is very important. Even though I think I
> have AMAF/ RAVE working now, it's still not so clear-cut what it's
> worth. With just 2,000 playouts I'm seeing a 88% win-rate against
> plain old UCT tree-search with
On Nov 30, 2008, at 11:49 AM, Mark Boon <[EMAIL PROTECTED]>
wrote:
Indeed, the scaling question is very important. Even though I think
I have AMAF/ RAVE working now, it's still not so clear-cut what it's
worth. With just 2,000 playouts I'm seeing a 88% win-rate against
plain old UCT tree-
> From: Don Dailey <[EMAIL PROTECTED]>
>
> I've always had this idea that the best way to build an opening book is
> the best way to build a general playing engine. You are trying to
> solve the same exact problem - what is the best move in this position?
When building an opening book, you have
Indeed, the scaling question is very important. Even though I think I
have AMAF/ RAVE working now, it's still not so clear-cut what it's
worth. With just 2,000 playouts I'm seeing a 88% win-rate against
plain old UCT tree-search without RAVE. At 10,000 playouts this win-
rate drops to 75%. W
On Sun, 2008-11-30 at 13:38 +0100, Olivier Teytaud wrote:
> But, it is also clearly established that the building of the opening
> book by self-play
> clearly works, whereas it is roughly the same idea. I guess the reason
> is the
> difference of strength of the player - a MCTS (Monte-Carlo Tree S
>
>
> By conjecture, i suppose you mean that
> no experiments yet has been ran as
> to assess this hypothesis ?
>
Yes. The other reasons were sufficient :-)
I think Sylvain (and maybe just everyone else) has tried
> at some point to use a UCT decision bot, as a way to
> get the simulation done.
Thanks a lot for your quick answer.
By conjecture, i suppose you mean that
no experiments yet has been ran as
to assess this hypothesis ?
I think Sylvain (and maybe just everyone else) has tried
at some point to use a UCT decision bot, as a way to
get the simulation done. Then using those high
>
> Is there any theoretical reasons for the Mogo Opening being built out of
> self play, rather than by spending time increasing the number of
> simulations
> at the root, and after a time, keeping what seems to be the best ?
>
There are practical reasons: our approach can be used with humans or
Hi.
Is there any theoretical reasons for the Mogo Opening being built out of
self play, rather than by spending time increasing the number of simulations
at the root, and after a time, keeping what seems to be the best ?
Obviously one could object that increasing the number of simulations
wou
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