On Mon, 2008-10-27 at 08:51 +0900, Darren Cook wrote:
> >>... the average game-length played was 119 moves. ...
> > ...
> > 111 is for random games. What the bots actually do is far from random.
>
> Or perhaps, if they can make a 9x9 game last 119 moves, it is not *that*
> far from random ;-).
I
On Sun, 2008-10-26 at 21:19 -0200, Mark Boon wrote:
> One more observation, something I found curious, is that according to
> the statistics twogtp put together, the average game-length played
> was 119 moves. I also noticed this was the number after the other two
> runs I had of 1,000 games
On Sun, 2008-10-26 at 21:10 -0200, Mark Boon wrote:
> When I look at CGOS right now my refbot TesujiRefBot has an ELO of
> 1286, JRef has 1290 and Cref has 1269. So evidence is mounting that
> my implementation, although completely different from yours, is
> conforming the definition you put
>>... the average game-length played was 119 moves. ...
> ...
> 111 is for random games. What the bots actually do is far from random.
Or perhaps, if they can make a 9x9 game last 119 moves, it is not *that*
far from random ;-).
Darren
___
computer-go
On Oct 26, 2008, at 7:19 PM, Mark Boon <[EMAIL PROTECTED]> wrote:
One more observation, something I found curious, is that according
to the statistics twogtp put together, the average game-length
played was 119 moves. I also noticed this was the number after the
other two runs I had of 1,00
One more observation, something I found curious, is that according to
the statistics twogtp put together, the average game-length played
was 119 moves. I also noticed this was the number after the other two
runs I had of 1,000 games each.
Since we made such a big deal about the average game
On 26-okt-08, at 20:38, Don Dailey wrote:
If you ran 10,000 games your score is amazingly close - you won't be
that close very often in 10,000 game samples. Of course I assume you
are testing this against a fully conforming version.
I don't know if it's so amazing. According to twogtp ther
If you ran 10,000 games your score is amazingly close - you won't be
that close very often in 10,000 game samples. Of course I assume you
are testing this against a fully conforming version.
So what exactly are you doing here to save time? My understanding is
that it has something to do with
On 25-okt-08, at 11:06, Don Dailey wrote:
I would be interested to see if your biased version can pass my
eventual
conformance tests. If it can, more power to you, I might use the
idea
myself.
I had it run 10,000 games over the weekend while away. The result is
49.9% (+/-0.5). I gue
I'm doing a small study of the scalability of the reference bot at
various numbers of playouts.
I'm also defining level 0 (or zero playouts) as meaning a legal
uniformly random move.
I still need a lot more games, but in general you eventually start to
see a point of diminishing returns for eac
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