On Wed, Apr 8, 2009 at 10:46 AM, Darren Cook wrote:
>> End game is another issue. MC programs only aim on winning, so they
>> endgame is nor perfect in sense human would define it, but perfect
>> enough to win if the game is winnable.
>
> You can modify komi to get the human expert and MC program
On Wed, Apr 8, 2009 at 10:46, Darren Cook wrote:
>> End game is another issue. MC programs only aim on winning, so they
>> endgame is nor perfect in sense human would define it, but perfect
>> enough to win if the game is winnable.
>
> You can modify komi to get the human expert and MC program in
> End game is another issue. MC programs only aim on winning, so they
> endgame is nor perfect in sense human would define it, but perfect
> enough to win if the game is winnable.
You can modify komi to get the human expert and MC program in agreement.
This suggests how you could automate a set o
2009/4/8 Zhiheng Zheng :
> I think most of test are designed by people who is stronger than best
> computer go program. So if MC program fail to pass a test, it is most likely
> MC is wrong. MC program is strong in some aspect, but week in other aspect.
> And the test suit is too focus on special
I think most of test are designed by people who is stronger than best
computer go program. So if MC program fail to pass a test, it is most likely
MC is wrong. MC program is strong in some aspect, but week in other aspect.
And the test suit is too focus on special aspect. We might split the test
Le 08/04/2009 à 07:28, Petri Pitkanen a écrit :
>
> This is nice idea and this is to a degree what GnuGo regression test
> does.
afaik, gnugo testsuite (based on a previous one) is not totally suitable
for MC programs, as some position are dead lost / clear win but shows
gg misbehavior.
Some
an
>> election. -
>> Otto von Bismarck
>>
>>
>> From: steve uurtamo
>> To: computer-go
>> Sent: Tuesday, April 7, 2009 5:12:27 PM
>> Subject: Re: [computer-go] Fast ways to evaluate program strength.
>>
>> otherw
arned from those test cases.
>
> Terry McIntyre
>
> -- People never lie so much as after a hunt, during a war or before an
> election. -
> Otto von Bismarck
>
> --
> *From:* steve uurtamo
> *To:* computer-go
> *Sent:* Tuesday, April
rom those test cases.
Terry McIntyre
-- People never lie so much as after a hunt, during a war or before an
election. -
Otto von Bismarck
From: steve uurtamo
To: computer-go
Sent: Tuesday, April 7, 2009 5:12:27 PM
Subject: Re: [computer-go] Fast ways
otherwise pair-go wouldn't be as funny to watch.
s.
On Tue, Apr 7, 2009 at 8:05 PM, Michael Williams
wrote:
> Łukasz Lew wrote:
>>
>> I would like to rephrase my question:
>> Let's measure prediction of pro moves of a whole engine while
>> modifying heavy playouts / MCTS in the engine.
>> How we
Łukasz Lew wrote:
I would like to rephrase my question:
Let's measure prediction of pro moves of a whole engine while
modifying heavy playouts / MCTS in the engine.
How well might it work?
Probably not well. Because what matters is not how often you play strong
moves, but how often you avoid
>>Another idea is to try to predict moves in a set of (pro) games.
>>Is the prediction rate well correlated with program strength?
>
> No, very poorly correlated. I think that's well known.
It is well known that systems created for pro move prediction using no
search like MoyoGo, similat Microsoft
From: Łukasz Lew
>I was wondering what are the good (fast/accurate) ways of evaluating
program strength.
>The most accurate one is to play many games against gnugo or on KGS.
>But it is quite slow as many games are needed.
>Another one is to have set of labeled positions (win/loss) and make
>
Łukasz Lew lukasz@gmail.com:
>Another one is to have set of labeled positions (win/loss) and make
>your program predict the labels. (This is what MoGo guys did)
>It is much faster. But how well it is correlated with the true strength?
I think it could be good if the set of positions is well-
Predicting pro-moves has very low correlation to strength. I think it is
next to useless. No experience but I think in this respect traditional
programs probably are better but certainly lose in strength. Cooperation
of moves is what matter not single moves.
Could be hard to come up with somet
Hi,
I was wondering what are the good (fast/accurate) ways of evaluating
program strength.
The most accurate one is to play many games against gnugo or on KGS.
But it is quite slow as many games are needed.
Another one is to have set of labeled positions (win/loss) and make
your program predict
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