Hello Yuandong,
thanks for your posting and welcome in the computer-go
mailing list.
I wish you and your team good luck for your further
attempts with darkfores***. Please, keep playing on KGS.
Ingo.
> Gesendet: Dienstag, 24. November 2015 um 21:45 Uhr
> Von: "Yuandong Tian"
Hi all,
I am the first author of Facebook Go AI. Thanks for your interest! This is
the first time I post a message here, so please forgive me if I mess up
with anything.
1. The estimation of 1d-2d is based on the win rate of free game in the
last 3 months (since darkforest launched in Aug). See
Perhaps bots in the style of Darkforest would be good
candidates to win the Handicap-29 prize...
http://www.althofer.de/handicap-29-prize.html
Ingo.
***
Gesendet: Dienstag, 24. November 2015 um 07:00 Uhr
Von: "David Fotland"
Hi,
Thank you for the paper.
Not only next move, but also opponent move and next counter move
prediction is very interesting.
I have two questions.
darkforest : standard features, 1 step prediction on KGS dataset
darkfores1 : extended features, 3 step prediction on GoGoD dataset
darkfores2 :
The December KGS bot tournament will be on Sunday, December 6th, starting at
16:00 UTC and ending by 22:00 UTC. It will use 13x13 boards, with time limits
of 9 minutes each plus fast Canadian overtime, and komi of 7.5.
Please register by emailing me, with the words "KGS Tournament Registration"
Thank you, Hideki, for pointing out my error. I hope the title of this
email helps to correct it.
Registration is now open for *two* KGS bot tournaments:
November 29 19x19, 14 minutes each, starts at 08:00 UTC, 12 rounds
December 06 13x13, 9 minutes each, starts at 16:00 UTC, 18
When I read about Facebook's DCNN-using go program, I remembered another
paper that I'd come across on arxiv, namely "How (not) to train your
generative model: scheduled sampling, likelihood, adversary?" by Ferenc
Huszar (http://arxiv.org/pdf/1511.05101.pdf).
A lot of that paper went over my head
On Mon, Nov 23, 2015 at 10:00:27PM -0800, David Fotland wrote:
> 1 kyu on KGS with no search is pretty impressive.
But it doesn't correlate very well with the reported results against
Pachi, it seems to me.
("Pachi 10k" should correspond to ~5s thinking time on 8-thread FX8350.)
> Perhaps
That can happen if the bot has a big (and strange) weak point such as
ladder. See attached record.
Hideki
Petr Baudis: <20151124123900.gm10...@machine.or.cz>:
>On Mon, Nov 23, 2015 at 10:00:27PM -0800, David Fotland wrote:
>> 1 kyu on KGS with no search is pretty impressive.
>
>But it doesn't
If you train your neural network on pro games, pros never play out
ladders that end up in capture, so when a ladder situation happens and
it gets played out, the running group is always safe. This is not the
case always, but you'd need to specifically play out the ladder to check.
On
10 matches
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