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
>
> 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
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
>
>
> 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.
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
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
> 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
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-
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
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
- 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
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
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 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
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
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