On Mon, Dec 15, 2008 at 11:10 PM, Mark Boon tesujisoftw...@gmail.com wrote:
It would have been much more persuasive if you had simply run a 5K
playout bot against a 100K bot and see which wins more.
In 200 games, 100k beat 5k a total of 127 times. So that's about a
63.5% win rate.
On Thu, 2008-12-18 at 03:27 +, Weston Markham wrote:
On Mon, Dec 15, 2008 at 11:10 PM, Mark Boon tesujisoftw...@gmail.com wrote:
It would have been much more persuasive if you had simply run a 5K
playout bot against a 100K bot and see which wins more.
In 200 games, 100k beat 5k a total
some day
someone will look-up at this particular experiment
and come out with the next computer-go revolution :)
Date: Mon, 15 Dec 2008 21:10:07 -0200
From: tesujisoftw...@gmail.com
To: computer-go@computer-go.org
Subject: Re: [computer-go] RefBot (thought-) experiments
Weston
Dec 2008 21:10:07 -0200
From: tesujisoftw...@gmail.com
To: computer-go@computer-go.org
Subject: Re: [computer-go] RefBot (thought-) experiments
Weston,
Although those result sound intriguing, it also looks like a
convoluted experiment. I wouldn't call gnu-go an expert judge,
although
On Tue, Dec 16, 2008 at 12:20 PM, Jason House
jason.james.ho...@gmail.com wrote:
When thinking about the apparent strength loss, I came up with a potential
theory: consistency. With more simulations, noise has less of an impact. I'm
going to guess that the known bias of AMAF leads to blunder
On Mon, 2008-12-15 at 17:30 -0500, Weston Markham wrote:
Out of 3637 matches using 5k playouts, jrefgo won (i.e., was ahead
after 10 moves, as estimated by gnugo) 1688 of them. (46.4%)
Out of 2949 matches using 100k playouts, jrefgo won 785. (26.6%)
It appears clear to me that increasing
On Mon, Dec 15, 2008 at 5:47 PM, Don Dailey drdai...@cox.net wrote:
Is Jrefgo the pure version that does not use tricks like the futures
map? If you use things like that, all bets are off - I can't be sure
this is not negatively scalable.
I don't know, although I was under the impression
On Tue, Dec 16, 2008 at 7:34 PM, Weston Markham
weston.mark...@gmail.com wrote:
And I believe that current
Monte Carlo methods only really manage to avoid the very worst of the
bad moves, regardless of how many playouts they run.
Um, perhaps I should qualify that as pure Monte Carlo, meaning
Weston Markham wrote:
I say 100K+ because I didn't set it to a specific number, just run as
many as it could within time allowed. Generally it would reach well
over 100K per move, probably more like 250K-500K. That should only
make things worse according to your hypothesis.
Yes, this is what
It would have been much more persuasive if you had simply run a 5K
playout bot against a 100K bot and see which wins more. ...
I may do that, although personally I would be far more cautious about
drawing conclusions from those matches, as compared to ones played
against a strong reference
On Wed, Dec 17, 2008 at 12:34 AM, Weston Markham
weston.mark...@gmail.com wrote:
I don't know, although I was under the impression that I had
downloaded the pure version. I found a reference to the source here
on the list, and downloaded and compiled that. When I get back home,
how would I
By the way, what does scratch100k.sh look like?
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On Wed, Dec 17, 2008 at 12:51 AM, Darren Cook dar...@dcook.org wrote:
I'd also like to second Mark Boon's statement that Gnugo is not an
expert judge, especially not after only 10 moves. One experiment I did,
a couple of years ago, was scoring lots of terminal or almost-terminal
9x9 positions
On Wed, Dec 17, 2008 at 1:32 AM, Mark Boon tesujisoftw...@gmail.com wrote:
By the way, what does scratch100k.sh look like?
../gogui-1.1.3/bin/gogui-twogtp -auto -black java -jar jrefgo.jar 10 -game
s 1 -komi 0.5 -maxmoves 10 -referee gnugo --mode gtp --score aftermath --ch
inese-rules
- Show quoted text -
On Tue, Dec 16, 2008 at 11:35 PM, Weston Markham
weston.mark...@gmail.com wrote:
On Wed, Dec 17, 2008 at 1:32 AM, Mark Boon tesujisoftw...@gmail.com wrote:
By the way, what does scratch100k.sh look like?
../gogui-1.1.3/bin/gogui-twogtp -auto -black java -jar jrefgo.jar
On Tue, 2008-12-16 at 19:34 -0500, Weston Markham wrote:
I may do that, although personally I would be far more cautious about
drawing conclusions from those matches, as compared to ones played
against a strong reference opponent. But I guess other people feel
differently about this. Anyway,
On Wed, Dec 17, 2008 at 2:07 AM, Mark Boon tesujisoftw...@gmail.com wrote:
Thanks. I just realized that you set the komi to 0.5. That doesn't
sound like a good idea. I wanted to make sure you had the same for the
100k version. Were your earlier experiments also with 0.5 komi? MC
programs are
On Tue, 2008-12-16 at 19:34 -0500, Weston Markham wrote:
I don't know, although I was under the impression that I had
downloaded the pure version. I found a reference to the source here
on the list, and downloaded and compiled that. When I get back home,
how would I quickly determine which
On Tue, Dec 16, 2008 at 7:34 PM, Weston Markham
weston.mark...@gmail.com wrote:
Incidentally, when I get home, I'll post the line of play that follows
those moves with the highest (asymptotic) Monte Carlo values,
according to jrefgo. I have about 18 moves calculated with high
accuracy.
Here
On Wed, Dec 17, 2008 at 2:38 AM, Don Dailey dailey@gmail.com wrote:
Is it the java version? I believe there is only one version of that and
it's the pure reference bot. I did make modification to a C version
but I think I kept that private.
Yes, it is the Java version.
Hi. This is a continuation of a month-old conversation about the
possibility that the quality of AMAF Monte Carlo can degrade, as the
number of simulations increases:
Me: running 10k playouts can be significantly worse than running 5k playouts.
On Tue, Nov 18, 2008 at 2:27 PM, Don Dailey
Is Jrefgo the pure version that does not use tricks like the futures
map? If you use things like that, all bets are off - I can't be sure
this is not negatively scalable.
You cannot draw any reasonable conclusions by stopping after 10 moves
and letting gnugo judge the game either.Why didn't
Weston,
Although those result sound intriguing, it also looks like a
convoluted experiment. I wouldn't call gnu-go an expert judge,
although it is an impartial one. The fact that it says that the 5K
ref-bot is ahead after 10 moves 46% of the time alone makes it suspect
in my eyes. But it is
On Mon, Nov 17, 2008 at 11:13 PM, Michael Williams
[EMAIL PROTECTED] wrote:
No one ever alleged that pure AMAF or pure MC was infinitely scalable.
My point is that in many cases, they doesn't even keep all of their
benefits, after some number of trials have been run. So, running 10k
playouts
Weston Markham wrote:
On Mon, Nov 17, 2008 at 11:13 PM, Michael Williams
[EMAIL PROTECTED] wrote:
No one ever alleged that pure AMAF or pure MC was infinitely scalable.
My point is that in many cases, they doesn't even keep all of their
benefits, after some number of trials have been run.
On Tue, 2008-11-18 at 12:02 -0500, Michael Williams wrote:
Weston Markham wrote:
On Mon, Nov 17, 2008 at 11:13 PM, Michael Williams
[EMAIL PROTECTED] wrote:
No one ever alleged that pure AMAF or pure MC was infinitely scalable.
My point is that in many cases, they doesn't even keep all
On Tue, Nov 18, 2008 at 12:02 PM, Michael Williams
[EMAIL PROTECTED] wrote:
It doesn't make any sense to me from a theoretical perspective. Do you have
empirical evidence?
I used to have data on this, from a program that I think was very
nearly identical to Don's reference spec. When I get a
On Tue, 2008-11-18 at 14:17 -0500, Weston Markham wrote:
On Tue, Nov 18, 2008 at 12:02 PM, Michael Williams
[EMAIL PROTECTED] wrote:
It doesn't make any sense to me from a theoretical perspective. Do you have
empirical evidence?
I used to have data on this, from a program that I think
On Tue, 2008-11-18 at 19:42 +, Oliver Lewis wrote:
It doesn't make any sense to me from a theoretical
perspective. Do you have empirical evidence?
I agree that empirical evidence is required, but theoretically, if MC
converges to something that is not perfect play,
From: Oliver Lewis [EMAIL PROTECTED]
On 11/18/08, Michael Williams [EMAIL PROTECTED] wrote:
It doesn't make any sense to me from a theoretical perspective. Do you have
empirical evidence?
I agree that empirical evidence is required, but theoretically,
You'll probably have to test more than one percentage on each type. It's possible (and likely, I think) that 50% could result in worse play while something
like 20% results in better play. Also, I'd like to re-submit my idea of increasing that number as the playout progresses.
Mark Boon
On 17-nov-08, at 13:36, Michael Williams wrote:
You'll probably have to test more than one percentage on each
type. It's possible (and likely, I think) that 50% could result in
worse play while something like 20% results in better play. Also,
I'd like to re-submit my idea of increasing
My reasoning is that more deterministic playouts are going to be stronger playouts (assuming they are done right), and so should contain less noise. But
because you don't want to be playing the same playout over and over, you need plenty of randomness near the start of the playout.
Mark Boon
On 17-nov-08, at 14:42, Michael Williams wrote:
My reasoning is that more deterministic playouts are going to be
stronger playouts (assuming they are done right), and so should
contain less noise. But because you don't want to be playing the
same playout over and over, you need plenty of
On Mon, 2008-11-17 at 13:17 -0200, Mark Boon wrote:
1- Capture a stone in atari with a certain probability (like David
Fotland says he's doing).
2- Forbid playing on the 1st or 2nd line unless there's a stone
within manhatten-distance 2.
3- Forbid putting yourself into atari with a
On 17-nov-08, at 15:33, Don Dailey wrote:
On Mon, 2008-11-17 at 13:17 -0200, Mark Boon wrote:
1- Capture a stone in atari with a certain probability (like David
Fotland says he's doing).
2- Forbid playing on the 1st or 2nd line unless there's a stone
within manhatten-distance 2.
3- Forbid
On Mon, 2008-11-17 at 16:04 -0200, Mark Boon wrote:
On another note, as an experiment I have a bot running on CGOS that
is the ref-bot but instead of using a fixed number of simulations I
use a fixed amount of time that slowly diminishes towards the end of
the game. The result is it does
On Mon, Nov 17, 2008 at 2:30 PM, Don Dailey [EMAIL PROTECTED] wrote:
On Mon, 2008-11-17 at 16:04 -0200, Mark Boon wrote:
On another note, as an experiment I have a bot running on CGOS that
is the ref-bot but instead of using a fixed number of simulations I
use a fixed amount of time that
Weston Markham wrote:
I think that I have seen this sort of thing with Monte Carlo programs,
and I think it is possible to get even less than almost nothing.
You may be getting overly-precise measurements of the Monte Carlo
values of the moves near the beginning of the game, so that the played
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