Re: [Computer-go] 9x9 is last frontier?

[Aja Huang]

2018-03-02 16:07 GMT+00:00 Dan :

> Hello Aja,
>
> Could you enlighten me on how AlphaZero handles tactics in chess ?
>
> It seems the mcts approach as described in the paper does not perform well
> enough.
>
> Leela-chess is not performing well enough even though leela-go seems to be
> doing well.
>
> Daniel
>
>
Hi, I've moved to other project so I can't really your question.
Remi's latest version of Crazy Stone seems to be much stronger than the
current version of Leela Zero. You might want to ask him for some insights
on your question.

Regards,
Aja


>
>
>
> On Fri, Mar 2, 2018 at 4:52 AM, Aja Huang  wrote:
>
>>
>>
>> 2018-03-02 6:50 GMT+00:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:
>>
>>> Von: "David Doshay" 
>>> > Go is hard.
>>> > Programming is hard.
>>> >
>>> > Programming Go is hard squared.
>>> > ;^)
>>>
>>> And that on square boards.
>>> Mama mia!
>>>
>>
>> Go is hard for humans, but in my own opinion I think Go seems to be too
>> easy for deep learning. So is programming Go now. :)
>>
>> Aja
>>
>>
>>>
>>> ;-) Ingo.
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Re: [Computer-go] 9x9 is last frontier?

[Aja Huang]

2018-03-02 6:50 GMT+00:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:

> Von: "David Doshay" 
> > Go is hard.
> > Programming is hard.
> >
> > Programming Go is hard squared.
> > ;^)
>
> And that on square boards.
> Mama mia!
>

Go is hard for humans, but in my own opinion I think Go seems to be too
easy for deep learning. So is programming Go now. :)

Aja


>
> ;-) Ingo.
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Re: [Computer-go] action-value Q for unexpanded nodes

[Aja Huang]

2017-12-06 13:52 GMT+00:00 Gian-Carlo Pascutto :

> On 06-12-17 11:47, Aja Huang wrote:
> > All I can say is that first-play-urgency is not a significant
> > technical detail, and what's why we didn't specify it in the paper.
>
> I will have to disagree here. Of course, it's always possible I'm
> misunderstanding something, or I have a program bug that I'm mixing up
> with this.
>

No matter I agree with you or not, unfortunately it's not up to me to
decide whether I can answer the question, even if I am personally happy to
(in fact, this post might be already exceeding my barrier a bit). I hope
you understand, and good luck with making it works.

I'm very happy the two Go papers we published have helped the Go community.
My dream was fulfilled and I've switched to pursue other challenges. :)

Aja


> Or maybe you mean that you expect the program to improve regardless of
> this setting. In any case, I've now seen people state here twice that
> this is detail that doesn't matter. But practical results suggest
> otherwise.
>
> For a strong supervised network, FPU=0 (i.e. not exploring all successor
> nodes for a longer time, relying strongly on policy priors) is much
> stronger. I've seen this in Leela Zero after we tested it, and I've
> known it to be true from regular Leela for a long time. IIRC, the strong
> open source Go bots also use some form of progressive widening, which
> produces the same effect.
>
> For a weak RL network without much useful policy priors, FPU>1 is much
> stronger than FPU=0.
>
> Now these are relative scores of course, so one could argue they don't
> affect the learning process. But they actually do that as well!
>
> The new AZ paper uses MCTS playouts = 800, and plays proportionally
> according to MCTS output. (Previous AGZ had playouts = 1600,
> proportional for first 30 moves).
>
> Consider what this means for the search probability outputs, exactly the
> thing the policy network has to learn. With FPU=1, the move
> probabilities are much more uniform, and the moves played are
> consequentially much more likely to be bad or even blunders, because
> there are less playouts that can be spent on the best move, even if it
> was found.
>
> > The initial value of Q is not very important because Q+U is
> > dominated by the U piece when the number of visits is small.
>
> a = Q(s, a) + coeff * P(s,a) * (sqrt(parent->visits) / 1.0f +
> child->visits());
>
> Assume parent->visits = 100, sqrt = 10
> Assume child->visits = 0
> Assume P(s, a) = 0.0027 (near uniform prior for "weak" network)
>
> The right most side of this (U term) is ~1. This clearly does not
> dominate the Q term. If Q > 1 (classic FPU) then every child node will
> get expanded. If Q = 0 (Q(s, a) = 0) then the first picked child
> (largest policy prior) will get something like 10 expansions before
> another child gets picked. That's a massive difference in search tree
> shape, *especially* with only 800 total playouts.
>
> --
> GCP
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Re: [Computer-go] action-value Q for unexpanded nodes

[Aja Huang]

2017-12-06 9:23 GMT+00:00 Gian-Carlo Pascutto :

> On 03-12-17 17:57, Rémi Coulom wrote:
> > They have a Q(s,a) term in their node-selection formula, but they
> > don't tell what value they give to an action that has not yet been
> > visited. Maybe Aja can tell us.
>
> FWIW I already asked Aja this exact question a bit after the paper came
> out and he told me he cannot answer questions about unpublished details.
>

Yes, I did ask my manager if I could answer your question but he
specifically said no. All I can say is that first-play-urgency is not a
significant technical detail, and what's why we didn't specify it in the
paper.

Aja



> This is not very promising regarding reproducibility considering the AZ
> paper is even lighter on them.
>
> Another issue which is up in the air is whether the choice of the number
> of playouts for the MCTS part represents an implicit balancing between
> self-play and training speed. This is particularly relevant if the
> evaluation step is removed. But it's possible even DeepMind doesn't know
> the answer for sure. They had a setup, and they optimized it. It's not
> clear which parts generalize.
>
> (Usually one wonders about such things in terms of algorithms, but here
> one wonders about it in terms of hardware!)
>
> --
> GCP
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Re: [Computer-go] November KGS bot tournament

[Aja Huang]

2017-10-27 8:15 GMT+01:00 Rémi Coulom :

> Hi,
>
> I would like to thank Nick very much, too. When I was working on Crazy
> Stone, these tournaments were a great source of motivation and enjoyment.
> I'll keep particularly good memories of the KGS-tournament parties in Tokyo.
>
> So, thanks Nick for a great contribution to our community.
>

At the end of my computer Go research career, I also want to thank Nick for
his contributions on consistently organizing KGS tournaments. When I was
working on Erica it was a lot of fun to watch and compete in those
9x9/13x13/19x19 events.

Aja



> Rémi
>
> - Mail original -
> De: "Hiroshi Yamashita" 
> À: computer-go@computer-go.org
> Envoyé: Vendredi 27 Octobre 2017 01:06:49
> Objet: Re: [Computer-go] November KGS bot tournament
>
> Hi Nick,
>
> > this will be the last of the series of KGS bot tournaments.
>
> Thank you for holding KGS tournament since 2005.
> On CGOS, there are always some new comers.
> I hope they also enter KGS bot tournament.
>
> Thanks,
> Hiroshi Yamashita
>
>
> - Original Message -
> From: "Nick Wedd" 
> To: 
> Sent: Thursday, October 26, 2017 4:43 PM
> Subject: [Computer-go] November KGS bot tournament
>
>
> The November KGS bot tournament will be on Sunday, November 5th, starting
> at 16:00 UTC and ending by 22:00 UTC.  It will use 19x19 boards, with
> time limits
> of 14 minutes each and very fast Canadian overtime, and komi of 7½.  It
> will be a Swiss tournament.  See http://www.gokgs.com/tournInfo.jsp?id=112
> 7
>
> Please register by emailing me at mapr...@gmail.com, with the words "KGS
> Tournament Registration" in the email title.
> With the falling interest in these events since the advent of AlphaGo, it
> is likely that this will be the last of the series of KGS bot tournaments.
>
> Nick
> --
> Nick Wedd  mapr...@gmail.com
>
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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Aja Huang]

2017-10-23 0:29 GMT+01:00 Lucas Baker :

> Hi Robert,
>
> The AlphaGo Zero games are free to use with proper attribution, so please
> use them as you like for commentaries as long as you credit DeepMind.
>

Yes, Lucas is right. We hope you enjoy AlphaGo Zero games. :)

Best regards,
Aja



> Best,
> Lucas Baker
>
> On Sun, Oct 22, 2017 at 3:59 PM Robert Jasiek  wrote:
>
>> AlphaGo Zero games are available as zipped SGF from Deepmind at
>> http://www.alphago-games.com/ For earlier AlphaGo games, I have seen
>> statements from Deepmind encouraging free use (presuming stating origin,
>> of course) so that the games may be commented etc. I cannot find a
>> similar statement from Deepmind for the published AlphaGo Zero games.
>> Are they for free use or copyrighted? I hope the former so everybody
>> including Deepmind can see more commentaries.
>>
>> --
>> robert jasiek
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Re: [Computer-go] AlphaGo Zero

[Aja Huang via Computer-go]

On Thu, Oct 19, 2017 at 11:04 AM, Hiroshi Yamashita 
wrote:

> I have two questions.
>
> 2017 Jan, Master , defeat 60 pros in a row.
> 2017 May, Master?, defeat Ke Jie 3-0.
>
> Master is Zero method with rollout.
> Zero   is Zero method without rollout.
>
> Did AlphaGo that played with Ke Jie use rollout?
> Is Zero with rollout stronger than Zero without rollout?
>

Hi Hiroshi,

I think these are good questions. You can ask them at
https://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/

Aja


> Thanks,
> Hiroshi Yamashita
>
> - Original Message - From: 
> To: 
> Sent: Thursday, October 19, 2017 2:50 AM
> Subject: [Computer-go] AlphaGo Zero
>
>
>
>> https://deepmind.com/blog/
>>
>> http://www.nature.com/nature/index.html
>>
>> Impressive!
>>
>
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Re: [Computer-go] AlphaGo Retires

[Aja Huang]

2017-05-28 23:14 GMT+08:00 Petr Baudis :

>   After winning against Ke Jie 3-0
>
> https://deepmind.com/research/alphago/alphago-china/
>
> AlphaGo retires from competitive Go.  The team released 50 selfplay
> games, will work on a teaching tool and publishing a second paper.
>
> https://blog.google/topics/google-asia/alphagos-next-move/
> https://deepmind.com/research/alphago/alphago-vs-alphago-
> self-play-games/
>
>   Congratulations and thank you, AlphaGo team, for your open scientific
> approach and publishing your methods.  The Computer Go community learned
> a lot as well, as evident from nowadays high-ranked KGS bots.
>

Thanks Petr. I hope one day pachi will reach KGS 9d, too. :)

Aja


>
> --
> Petr Baudis, Rossum.ai
> Run before you walk! Fly before you crawl! Keep moving forward!
> If we fail, I'd rather fail really hugely.  -- Moist von Lipwig
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Re: [Computer-go] Ke Jie vs. AlphaGo match

[Aja Huang via Computer-go]

Hi Rémi,

On Fri, May 19, 2017 at 5:25 PM, Rémi Coulom  wrote:

> Hi Aja,
>
> Can you tell us whether there will be any live broadcast?
>

As Hiroshi pointed out, please visit DeepMind's website for latest update
on live broadcast. Cray Stone's live comment will be interesting. :)

Aja


> I read it on reddit:
> https://www.reddit.com/r/baduk/comments/6bxt5i/are_the_
> alphago_matches_being_shown_live_on/
>
> "American in China here: apparently China has banned all commentary and
> live streams of the matches, and the city is pretty much on lock down and
> near impossible to get invited into. As for viewing, I'm trying to work out
> exactly what this means. For sure we will have the game records, but it's
> unclear if the ban will hold for foreign broadcasts like the AGA's or only
> Chinese owned broadcasts."
>
> That seems unbelievable. I'd like to set up live comment by Crazy Stone on
> the Crazy Sensei web site.
>
> Rémi
>
> - Mail original -
> De: "Aja Huang" 
> À: computer-go@computer-go.org
> Envoyé: Vendredi 19 Mai 2017 05:52:41
> Objet: Re: [Computer-go] Ke Jie vs. AlphaGo match
>
>
>
> Thanks Hiroshi. I hope you will enjoy AlphaGo's games.
>
>
> Aja
>
>
> 2017-05-19 11:28 GMT+08:00 Hiroshi Yamashita < y...@bd.mbn.or.jp > :
>
>
> Hi,
>
> It will be played in a week.
> But there are few information about this.
> Is there YouTube live available?
>
> I found a schedule in Panda-net site.
>
> Ke Jie vs. AlphaGo (3 hours + 1 minute x5)
> Game1 May 23 11:30-18:30
> Game2 May 25 11:30-18:30
> Game3 May 27 11:30-18:30
>
> Pair Go May 26 09:30-12:30
> Team Go May 26 13:30-19:30
>
> Panda net (in Japanese)
> http://www.pandanet.co.jp/event/fogs/
> Exploring the mysteries of Go with AlphaGo and China's top players
> https://deepmind.com/blog/exploring-mysteries-alphago/
>
> Thanks,
> Hiroshi Yamashita
>
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Re: [Computer-go] Ke Jie vs. AlphaGo match

[Aja Huang]

Thanks Hiroshi. I hope you will enjoy AlphaGo's games.

Aja

2017-05-19 11:28 GMT+08:00 Hiroshi Yamashita :

> Hi,
>
> It will be played in a week.
> But there are few information about this.
> Is there YouTube live available?
>
> I found a schedule in Panda-net site.
>
> Ke Jie vs. AlphaGo  (3 hours + 1 minute x5)
> Game1May 23   11:30-18:30
> Game2May 25   11:30-18:30
> Game3May 27   11:30-18:30
>
> Pair Go  May 26   09:30-12:30
> Team Go  May 26   13:30-19:30
>
> Panda net (in Japanese)
> http://www.pandanet.co.jp/event/fogs/
> Exploring the mysteries of Go with AlphaGo and China's top players
> https://deepmind.com/blog/exploring-mysteries-alphago/
>
> Thanks,
> Hiroshi Yamashita
>
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Re: [Computer-go] Welcome to Download the "computer go dataset" (160K 9D vs 9D SGFs)

[Aja Huang via Computer-go]

On Mon, Apr 17, 2017 at 4:19 PM, Yu Yuan  wrote:

> 2015.11.02 - 2016.12.31
> TYGEM "9D vs 9D" dataset (1,516,031 games).
>
> 2003.09.25 - 2011.12.28
> TOM "9D vs 9D" dataset (50,956 games).
>

Hi Yu Yuan, I was wondering did you check with TYGEM and TOM about the
copyright of these SGFs?

Aja



> https://github.com/yenw/computer-go-dataset 
>
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Re: [Computer-go] Zen lost to Mi Yu Ting

[Aja Huang via Computer-go]

On Tue, Mar 21, 2017 at 10:48 AM, Hideki Kato 
wrote:

> The value network has been trained with Chinese rules and 7.5
> pts komi.  Using this for Japanese and 6.5, there will be some
> error in close games.  We knew this issue and thought such
> chances would be so small that postponed correcting (not so
> easy).
>

Oh, so that's why! Good luck with Zen's next two games.

Aja


> Best,
> Hideki
>
> Pawe  Morawiecki:  4wsxoezykbheslprewci...@mail.gmail.com>:
> >Hi,
> >
> >After an interesting game DeepZen lost to Mi Yu Ting.
> >Here you can replay the complete game:
> >http://duiyi.sina.com.cn/gibo_new/live/viewer.asp?sno=13
> >
> >According to pro experts, Zen fought really well, but it seems there is
> >still some issue how Zen (mis)evaluates its chances. At one point it
> showed
> >84% chance of winning (in the endgame), whereas it was already quite clear
> >Zen is little behind (2-3 points).
> >
> >Regards,
> >Pawel
> > inline file
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Re: [Computer-go] Leela Superstar!

[Aja Huang via Computer-go]

Congrats for Leela's significant improvements. :)

Aja

On Tue, Feb 21, 2017 at 1:25 PM, Gian-Carlo Pascutto  wrote:

> On 19-02-17 17:00, "Ingo Althöfer" wrote:
> > Hi,
> > the rank graph of LeelaX on KGS looks impressive:
> >
> > http://www.dgob.de/yabbse/index.php?action=dlattach;
> topic=6048.0;attach=5658;image
> >
> > Of course, its shape will be more "gnubbled" after a few days.
>
> Thank you for the kind words, it is appreciated.
>
> I need my machine back to try a few more things, so Leela won't be on
> KGS for a while. (I was surprised to see it on CGOS - it's not my account).
>
> I did not register for the UEC Cup. I seem to be in good company there,
> sadly.
>
> --
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Re: [Computer-go] NATURE: Top 10 people 2016

[Aja Huang]

On Mon, Dec 19, 2016 at 6:46 PM, "Ingo Althöfer" <3-hirn-ver...@gmx.de>
wrote:

> Hi,
> NATURE published a list of top 10 people in science 2016:
> http://www.nature.com/news/nature-s-10-1.21157
>
> Demis Hassabis, manager of the AlphaGo team, is in the list.
>

Thanks Ingo. Yes, I think Demis is worthy of the honor. :)

Aja


> Congratulations!
>
> Ingo.
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Re: [Computer-go] "Google event match" in October?

[Aja Huang]

Hi Paweł,

On Thu, Sep 15, 2016 at 9:12 AM, Paweł Morawiecki <
pawel.morawie...@gmail.com> wrote:

> Dear Aja,
>
> On the _official_ website of Korean Baduk Association, specifically on
> their schedule (http://www.baduk.or.kr/info/schedule.asp), they announce
> what is called "Google event match(es) in October. Looks like 3 matches, 3
> rounds per match. Dates are as follows:
>
> 10/03 1st match 1st Round
> 10/04 1st match 2nd Round
> 10/17 1st match 3rd Round
>
> 10/18 2nd match 1st Round
> 10/24 2nd match 2nd Round
> 10/25 2nd match 3rd Round
>
> 10/31 3rd match 1st Round
> 11/01 3rd match 2nd Round
> 11/02 3rd match 3rd Round
>
> Is it AlphaGo playing, perhaps with 3 players? Or it is just some event
> (tournament) which is sponsored by Google?
>

No, it's not AlphaGo. I don't know any detail of this event.

Aja



>
>
Best regards,
> Paweł
>
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Re: [Computer-go] AlphaGo selfplay 3 games

[Aja Huang]

Hi Richard,

On Wed, Sep 14, 2016 at 3:50 PM, Richard Lorentz 
wrote:

> Hi Aja,
>
> Now I see.
>
> Also, I've looked at some of the games and the commentary is really very
> nice.
>
> Thanks!
>
> -Richard
>
> P.S. Nice seeing you (again?!) in Leiden!
>

It was nice to see you, too. I'm glad you enjoy the commentary. :)

Aja

>
>
>
> On 09/14/2016 07:41 AM, Aja Huang wrote:
>
> Hi Richard,
>
> On Wed, Sep 14, 2016 at 2:54 PM, Richard Lorentz  > wrote:
>
>> After following the link and then playing out one of the games in my
>> browser (Chrome) I don't see the variations. Can anyone help me figure out
>> how to make them visible?
>>
>
> You can see the variations by clicking on labels on the board A, B, etc.
>
> Aja
>
>
>
>> Thanks.
>>
>> -Richard
>>
>>
>> On 09/14/2016 02:25 AM, Hiroshi Yamashita wrote:
>>
>> Hi,
>>
>> DeepMind published AlphaGo's selfplay 3 games with comment.
>>
>> AlphaGo Games - English
>> https://urldefense.proofpoint.com/v2/url?u=https-3A__deepmin
>> d.com_research_alphago_alphago-2Dgames-2Denglish_&d=DQIGaQ&c
>> =Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U&r=NbBKrBPWWTAfp
>> _een5wnh2rUKa2Rtv0nPE0GGyoIUSQ&m=wa5KR9ywvzFZsR_
>> 3EMUX4SOzu3Nnkk8YamJJk9-VcvU&s=XVTGs4Q_7WgeaciJv6vGgubF5P5O
>> jid_z-I23Mh53RI&e=
>> OHASHI Hirofumi 6d pro said
>> "I understood AlphaGo is extremely strong even if 5sec/move."
>>
>> Thanks
>> Hiroshi Yamashita
>>
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>> k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U&r=NbBKrBPWWTAfp_een
>> 5wnh2rUKa2Rtv0nPE0GGyoIUSQ&m=wa5KR9ywvzFZsR_3EMUX4SOzu3Nnkk
>> 8YamJJk9-VcvU&s=L3AdTXuLmofJxLYOu0XyHEn4Dg5TP5MZCPZetJb0JWw&e=
>>
>>
>>
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>>
>
>
>
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Re: [Computer-go] AlphaGo selfplay 3 games

[Aja Huang]

Hi Richard,

On Wed, Sep 14, 2016 at 2:54 PM, Richard Lorentz 
wrote:

> After following the link and then playing out one of the games in my
> browser (Chrome) I don't see the variations. Can anyone help me figure out
> how to make them visible?
>

You can see the variations by clicking on labels on the board A, B, etc.

Aja



> Thanks.
>
> -Richard
>
>
> On 09/14/2016 02:25 AM, Hiroshi Yamashita wrote:
>
> Hi,
>
> DeepMind published AlphaGo's selfplay 3 games with comment.
>
> AlphaGo Games - English
> https://urldefense.proofpoint.com/v2/url?u=https-3A__deepmin
> d.com_research_alphago_alphago-2Dgames-2Denglish_&d=DQIGaQ&
> c=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U&r=
> NbBKrBPWWTAfp_een5wnh2rUKa2Rtv0nPE0GGyoIUSQ&m=
> wa5KR9ywvzFZsR_3EMUX4SOzu3Nnkk8YamJJk9-VcvU&s=XVTGs4Q_7Wgeac
> iJv6vGgubF5P5Ojid_z-I23Mh53RI&e=
> OHASHI Hirofumi 6d pro said
> "I understood AlphaGo is extremely strong even if 5sec/move."
>
> Thanks
> Hiroshi Yamashita
>
> ___
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> Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U&r=NbBKrBPWWTAfp_
> een5wnh2rUKa2Rtv0nPE0GGyoIUSQ&m=wa5KR9ywvzFZsR_3EMUX4SOzu3Nn
> kk8YamJJk9-VcvU&s=L3AdTXuLmofJxLYOu0XyHEn4Dg5TP5MZCPZetJb0JWw&e=
>
>
>
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Re: [Computer-go] Very interesting presentation of Fan Hui about AlphaGo

[Aja Huang]

On Tue, Jul 26, 2016 at 1:47 PM, Aja Huang  wrote:
>
> four AlphaGo v18 vs. AlphaGo v18 self-play games
>

Correction: There will be three v18 self-play games, not four.

Regards,
Aja
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Re: [Computer-go] Very interesting presentation of Fan Hui about AlphaGo

[Aja Huang]

On Tue, Jul 26, 2016 at 12:55 PM, J. van der Steen <
j.van.der.st...@gobase.org> wrote:

>
> Dear Alexander Kozlovsky,
>
> Are the sheets of this presentation available somewhere by any chance. The
> sheets are a bit hard to read in the video on YouTube.
>

Hi,

Thanks for the interest. We are posting some AlphaGo commentaries (five Lee
Sedol match games and four AlphaGo v18 vs. AlphaGo v18 self-play games) in
few weeks after our website is done. The commentaries are edited by Fan Hui
2p with comments from Gu Li 9p and Zhou Ruiyang 9p based on a lot of
AlphaGo's data. The commentaries will be free for download.

You can find some slides on Fan Hui's talk at
http://slide.sports.sina.com.cn/go/slide_2_820_106702.html#p=1
http://tour.xaoyo.com/ty/20160726/788489.html

Regards,
Aja


> best regards,
> Jan van der Steen
>
> On 26-07-16 10:29, Alexander Kozlovsky wrote:
>
>> https://www.youtube.com/watch?v=V0-IWQ9TvLo&t=1545s
>>
>> The presentation was given at European Go Congress 2016 which currently
>> takes place in Saint-Petersburg.
>>
>> The video startsat 25:00
>> , and sounds
>> appears at 25:45 .
>> Fan Hui shows some very unorthodox moves considered by AlphaGo in
>> self-play games, and announced that in a few weeks there will be a site
>> with commentaries on match games between AlphaGo and Lee Sedol and also
>> on AlphaGo self-play games. The commentaries were compiled by Fan Hui 2p
>> with support of Gu Li 9p and Zhou Rulyang 9p.
>> Regards, Alexander Kozlovsky
>>
>>
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Re: [Computer-go] Timetable "Computers and Games 2016"

[Aja Huang]

2016-06-22 12:29 GMT+01:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:

> Hi,
>
> the timetable for the conference "Computers and Games 2016"
> in Leiden (NL) is online now. COnference days are June 29 -
> July 01.
>
>
> https://www.conftool.net/cg2016/index.php?page=browseSessions&print=yes&doprint=yes&presentations=show
>
> At least 8 of the talks (including the keynote presentation by Aja Huang)
> are directly related to Go and computer Go.
>

Thanks Ingo.

In my talk I will focus on the source of AlphaGo's power: policy and value
networks. I will also mention the status and conclusion of our
investigation on AlphaGo's problem in the 4th game against Lee Sedol.

Looking forward to meeting you all there.

Regards,
Aja


>
> Ingo.
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Re: [Computer-go] DarkForest is open-source now.

[Aja Huang]

Well done, Yuangdong. :)

Aja

2016-06-10 18:31 GMT+01:00 Yuandong Tian :

> Hi all,
>
> DarkForest Go engine is now public on the Github (pre-trained CNN models
> are also public). Hopefully it will help the community.
>
> https://github.com/facebookresearch/darkforestGo
>
> Thanks!
>
> Best,
> Yuandong
>
> 
> Yuandong Tian
> Research Scientist,
> Facebook Artificial Intelligence Research (FAIR)
> Website:
> https://research.facebook.com/researchers/1517678171821436/yuandong-tian/
>
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Re: [Computer-go] Hajin Lee will play a live commented game against CrazyStone

[Aja Huang]

On 23 May 2016 08:00, "Rémi Coulom"  wrote:
>
> I think one of the main problems is that the network learns good replies
to good moves. The training set does not have good replies to bad moves,
but the search tree is full of bad moves that need to be punished.

As the joseki that CS messed up was quite basic, it looks like a bug in
your search or a problem of your policy network. Maybe you should train and
run your networks on GPU.

Aja

> Alvaro's suggestion looks good. This is one of the experiments I want to
try.
>
> Rémi
>
>
> On 05/22/2016 11:41 PM, Henry Hemming wrote:
>>
>> If the network is too selective, the cost function used to generate it
doesn't penalize extreme predictions sufficiently? It was generated using
quadratic cost when it should have been using cross-entropy cost?
>>
>> On Mon, May 23, 2016 at 12:08 AM Álvaro Begué mailto:alvaro.be...@gmail.com>> wrote:
>>
>> Disclaimer: I haven't actually implemented MCTS with NNs, but I
>> have played around with both techniques.
>>
>> Would it make sense to artificially scale down the values before
>> the SoftMax is applied, so the probability distribution is not as
>> concentrated, and unlikely moves are not penalized as much?
>>
>>
>>
>> On Sun, May 22, 2016 at 3:54 PM, Rémi Coulom > > wrote:
>>
>> Hi,
>>
>> Thanks for using Crazy Stone.
>>
>> I tried changes during the week, but nothing worked. So the
>> version that played the game was almost identical to the
>> commercial version.
>>
>> The search did not anticipate Black E8 after B3. It seems the
>> NN makes the search too selective. I will investigate more.
>>
>> Rémi
>>
>> - Mail original -
>> De: "Paweł Morawiecki" > >
>> À: computer-go@computer-go.org
>> 
>>
>> Envoyé: Dimanche 22 Mai 2016 21:29:56
>> Objet: Re: [Computer-go] Hajin Lee will play a live commented
>> game against  CrazyStone
>>
>>
>>
>> Hi,
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> It's fun to hear the pro making comments as she goes. I had
>> hoped for a better game, though.
>> Any comments from the CS camp?
>>
>>
>>
>> I'm not from CrazyStone Team but a happy user of CS Deep
Learning.
>>
>>
>> I analyzed the game (30 000 playouts per move) with the
>> version commercially available and it got everything right. I
>> mean every move Hajin Lee questioned was also questioned by
>> CrazyStone running on my PC. It includes:
>>
>>
>> - questionable attachment in the first joseki they played
>> - hane in upper-left corner
>> - and finally a blunder at b3 (loosing move) and selection of
>> this joseki as well
>>
>>
>> Remi said that he's been working hard to improve CS over the
>> last week, but it looks like something went wrong and instead
>> he got clearly a weaker program. Particularly this B3 is
>> really strange, where right after this white catches
>> everything with one move. Remi, what went wrong?
>>
>>
>> Cheers,
>> Paweł
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> Thanks,
>> Álvaro.
>>
>>
>>
>>
>>
>>
>>
>>
>> On Mon, May 16, 2016 at 3:58 AM, Xavier Combelle <
>> xavier.combe...@gmail.com  >
>>
>> wrote:
>>
>>
>>
>> That's fantastic
>>
>>
>> I suppose crazystone will play with crazystone account, but
>> what will be her handle ?
>>
>>
>>
>>
>>
>> 2016-05-16 9:50 GMT+02:00 Rémi Coulom < remi.cou...@free.fr
>>  > :
>>
>>
>>
>> Hi,
>>
>> I am very happy to announce that Hajin Lee will play a live
>> commented game against Crazy Stone on Sunday, at 8PM Korean
>> time. The game will take place on KGS, and she will make live
>> comments on her youtube channel.
>>
>> Haylee's youtube:
>> https://www.youtube.com/c/HayleesWorldofGoBaduk
>>
>> Rémi
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Re: [Computer-go] Lee Sedol's reviews on AlphaGo games

[Aja Huang]

Hi Ingo,

2016-04-14 14:21 GMT+01:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:
>
> Is 102.R10 really so special? I analysed that position with CrazyStone2013
> about 30 times (independent runs). In 20+ % of the runs, CS2013 also wants
> to play R12 (giving White 60+% chances of winning). But also in the other
> cases, where mostly R14 or P17 are proposed, the expected winning chances
> for White are given by about 60 %.
>
> I would indeed like to know which % AlphaGo gave White in this position.
>

David Silver did show AlphaGo's value graph of game 1 in his talk in UCL.
AlphaGo's value at move 102 was higher than 60%. I'm not allowed to share
the graph now but I hope I have answered your question.

Aja
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Re: [Computer-go] BetaGo?

[Aja Huang]

On Tue, Apr 19, 2016 at 4:11 PM, Rémi Coulom  wrote:

> Anybody knows who is the author of BetaGo? It is playing with account
> GoBeta on KGS, and is 6d.
>

I can confirm it's not AlphaGo or a weaker version of AlphaGo. We haven't
decided to play AlphaGo online yet, but when the decision is made we will
use AlphaGo(P) on tygem and AlphaGoBot on KGS.

Aja


> I found this project:
> http://maxpumperla.github.io/betago/
>
> But it seems weak.
>
> Rémi
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Re: [Computer-go] Nice graph

[Aja Huang]

2016-03-26 2:48 GMT+00:00 Petr Baudis :
>
> The word covered by the speaker's head is "self".  Bot results in
> self-play are always(?) massively exaggerated.  It's not uncommon to see
> a 75% self-play winrate in selfplay to translate to 52% winrate against
> a third-party reference opponent.  c.f. fig 7&8 in
> http://pasky.or.cz/go/pachi-tr.pdf . Intuitively, I'd expect the effect
> to be less pronounced with very strong programs, but we don't know
> anything precise about the mechanics here and experiments are difficult.
>

Note that recently for Crazy Stone and Zen improvements in self-play also
transferred to playing strength against human players. According to Remi
and Hideki, Crazy Stone and Zen are both >=80% stronger with a policy net
and they both reach 7d on KGS (1 stone stronger).

But generally I agree that we should be cautious about self-play results.

Aja

It's no doubt today's AlphaGo is much stronger than the Nature version.
> But how much?  We'll have a better idea when they pit it in more matches
> with humans, and ideally when other programs catch up further.  Without
> knowing more (like the rest of the slides or a statement by someone from
> Deepmind), I wouldn't personally read much into this graph.
>
> --
> Petr Baudis
> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] Value Network

[Aja Huang]

Good stuff, Hiroshi. Looks like I don't need to answer the questions
regarding value network. :)

Aja

On Sat, Mar 19, 2016 at 9:23 PM, Hiroshi Yamashita  wrote:

> What are you using for loss?
>>
>
> I use this,
>
> layers {
>  name: "loss"
>  type: EUCLIDEAN_LOSS
>  bottom: "fc14"
>  bottom: "label"
>  top: "loss"
> }
>
> 
> name: "AyaNet"
> layers {
>  name: "mnist"
>  type: DATA
>  top: "data"
>  data_param {
>source: "train_i50_v_2k_leveldb"
> #backend: LMDB
>batch_size: 256
>  }
>  include: { phase: TRAIN }
> }
> layers {
>  name: "mnist"
>  type: HDF5_DATA
>  top: "label"
>  hdf5_data_param {
>source: "/home/yss/test/train_v_2k_i50_11_only_hdf5/aya_data_list.txt"
>batch_size: 256
>  }
>  include: { phase: TRAIN }
> }
> layers {
>  name: "mnist"
>  type: DATA
>  top: "data"
>  data_param {
>source: "test_i50_v_2k_leveldb"
> #backend: LMDB
>batch_size: 256
>  }
>  include: { phase: TEST }
> }
> layers {
>  name: "mnist"
>  type: HDF5_DATA
>  top: "label"
>  hdf5_data_param {
>source: "/home/yss/test/test_v_2k_i50_11_only_hdf5/aya_data_list.txt"
>batch_size: 256
>  }
>  include: { phase: TEST }
> }
>
>
> #this part should be the same in learning and prediction network
> layers {
>  name: "conv1_5x5_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "data"
>  top: "conv1"
>  convolution_param {
>num_output: 128
>kernel_size: 5
>pad: 2
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu1"
>  type: RELU
>  bottom: "conv1"
>  top: "conv1"
> }
>
> layers {
>  name: "conv2_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv1"
>  top: "conv2"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu2"
>  type: RELU
>  bottom: "conv2"
>  top: "conv2"
> }
>
> layers {
>  name: "conv3_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv2"
>  top: "conv3"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu3"
>  type: RELU
>  bottom: "conv3"
>  top: "conv3"
> }
>
> layers {
>  name: "conv4_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv3"
>  top: "conv4"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu4"
>  type: RELU
>  bottom: "conv4"
>  top: "conv4"
> }
>
>
> layers {
>  name: "conv5_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv4"
>  top: "conv5"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu5"
>  type: RELU
>  bottom: "conv5"
>  top: "conv5"
> }
>
> layers {
>  name: "conv6_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv5"
>  top: "conv6"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu6"
>  type: RELU
>  bottom: "conv6"
>  top: "conv6"
> }
>
> layers {
>  name: "conv7_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv6"
>  top: "conv7"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu7"
>  type: RELU
>  bottom: "conv7"
>  top: "conv7"
> }
>
> layers {
>  name: "conv8_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv7"
>  top: "conv8"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu8"
>  type: RELU
>  bottom: "conv8"
>  top: "conv8"
> }
>
> layers {
>  name: "conv9_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv8"
>  top: "conv9"
>  convolution_param {
>num_output: 128
>kernel_size: 3
>pad: 1
>weight_filler {
>  type: "xavier"
>}
>bias_filler {
>  type: "constant"
>}
>  }
> }
> layers {
>  name: "relu9"
>  type: RELU
>  bottom: "conv9"
>  top: "conv9"
> }
>
> layers {
>  name: "conv10_3x3_128"
>  type: CONVOLUTION
>  blobs_lr: 1.
>  blobs_lr: 2.
>  bottom: "conv9"
>  top: "conv10"
>  convolution_param {
>num_output: 128
>kernel_s

Re: [Computer-go] Zen19X achieved stable KGS 7d

[Aja Huang]

Congrats to Zen's achievements! :)

Aja

On Mon, Feb 1, 2016 at 11:44 AM, Petr Baudis  wrote:

> On Sun, Jan 31, 2016 at 10:16:25AM +0900, Hideki Kato wrote:
> > Petr Baudis: <20160130150502.gf12...@machine.or.cz>:
> > >  Hi,
> > >
> > >  it seems that Zen19X grabbed at KGS 7d and looks like it's gonna hold!
> > >
> > >
> http://www.gokgs.com/gameArchives.jsp?user=zen19x&year=2016&month=1&oldAcounts=y
> > >
> > >It's fairly fast, but not terribly so, and while it's unfortunate most
> of
> > >the games are handicaps against low dans, there probably isn't any other
> > >way on KGS these days.
> > >
> > >  Congratulations to the Zen team!  Weren't AlphaGo announced pretty
> > >much at the same moment Zen19X started playing, this would be a really
> > >huge news, bumping the state-of-art by two KGS ranks; this was quite
> > >an unlucky timing for Zen...  Still, a treat for KGS denizens. :-)
> >
> > Thanks Pasky, but the timing could be the worst :).  It took
> > almost 4 years from 6d to 7d.  One good thing is that Zen19X is
> > running on not a clsuter but just a dual-Xeon (2 x 12 core)
> > server.  The kiblitz asked me about AlphaGo but that's all.
> > Their support for Zen is unchanged at all because, I guess, they
> > can't play with AlphaGo but Zen and either is strong enough for
> > them.  There were more than 120 watchers on a game with an 8d.
>
> Yesterday, this Zen version has beaten Pavol Lisy 1p in a no komi game!
> It was a blitz game, but imho this was unthinkable until very recently
> anyway:
>
> http://www.lifein19x19.com/forum/viewtopic.php?p=198362#p198362
> http://files.gokgs.com/games/2016/1/31/cheater-Zen19X.sgf
>
> These are great times for Computer Go. :)
>
> --
> Petr Baudis
> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Aja Huang]

2016-01-27 18:46 GMT+00:00 Aja Huang :

> Hi all,
>
> We are very excited to announce that our Go program, AlphaGo, has beaten a
> professional player for the first time. AlphaGo beat the European champion
> Fan Hui by 5 games to 0. We hope you enjoy our paper, published in Nature
> today. The paper and all the games can be found here:
>
> http://www.deepmind.com/alpha-go.html
>

The paper is freely available to download at the bottom of the page.
https://storage.googleapis.com/deepmind-data/assets/papers/deepmind-mastering-go.pdf

Aja


> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
> March, to see whether we finally have a Go program that is stronger than
> any human!
>
> Aja
>
> PS I am very busy preparing AlphaGo for the match, so apologies in advance
> if I cannot respond to all questions about AlphaGo.
>
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Re: [Computer-go] Number of Go positions computed at last

[Aja Huang]

Very interesting. Thanks John. :)

Aja

On Fri, Jan 22, 2016 at 4:18 AM, John Tromp  wrote:

> It's been a long journey, and now it's finally complete!
>
> http://tromp.github.io/go/legal.html
>
> has all the juicy details...
>
> regards,
> -John
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Re: [Computer-go] 7x7 Go is weakly solved

[Aja Huang]

Hi Erik,

On Mon, Nov 30, 2015 at 10:37 AM, Erik van der Werf <
erikvanderw...@gmail.com> wrote:

> Hi Aja,
>
> This result seems consistent with earlier claimed human solutions for 7x7
> dating back to 1989. So what exactly is new? Did he write a program that
> actually calculates the value?
>

Did you mean 7x7 Go was weakly solved before?

Aja


> Best,
> Erik
>
>
> On Mon, Nov 30, 2015 at 2:10 AM, Aja Huang  wrote:
>
>> It's the work by Chinese pro Li Zhe 7p.
>> http://blog.sina.com.cn/s/blog_53a2e03d0102vyt5.html
>>
>> His conclusions on 7x7 Go board:
>> 1. Optimal komi is 9.0.
>> 2. Optimal solution is not unique. But the first 3 moves are unique, and
>> the first 7 moves generate 5 major optimal solutions.
>> 3. There are many variations not affecting final score.
>>
>> Aja
>>
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[Computer-go] 7x7 Go is weakly solved

[Aja Huang]

It's the work by Chinese pro Li Zhe 7p.
http://blog.sina.com.cn/s/blog_53a2e03d0102vyt5.html

His conclusions on 7x7 Go board:
1. Optimal komi is 9.0.
2. Optimal solution is not unique. But the first 3 moves are unique, and
the first 7 moves generate 5 major optimal solutions.
3. There are many variations not affecting final score.

Aja
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Re: [Computer-go] Komi 6.5/7.5

[Aja Huang]

2015-11-06 9:41 GMT+00:00 Robert Jasiek :

> On 06.11.2015 10:35, Aja Huang wrote:
>
>> another very strong Chinese pro Shi Yue
>> <http://www.goratings.org/players/449.html> (No.3 at Go ratings
>> http://www.goratings.org/, current No.2 in China) said he likes to take
>> White with komi 7.5, but with komi 6.5 in his opinion it's 50-50.
>>
>
> He might as well have expressed the opinion that with 5.5 it was 50-50.
> (Chinese pro: area scoring.)


That's a good point. I should have added that both Ke Jie and Shi Yue were
both assuming Chinese rules using area scoring, in which case the score is
almost always odd.

Aja


>
> --
> robert jasiek
>
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Re: [Computer-go] Report KGS Slow Tournament

[Aja Huang]

2015-09-26 2:48 GMT+01:00 Hideki Kato :

> Aja Huang: <
> calqb9_kdkgkxsvrjksjmnvywe3tpjw1ct_n-tpjt_sdmmit...@mail.gmail.com>:
> >Congrats to Abakus. Does anyone know who is Abakus` author?
>
> Tobias Graf is the author.
> See http://www.computer-go.info/db/operson.php?a=Graf%2C+Tobias


I see, thanks. It's a strong program from Germany. :)

Aja


> Hideki
>
> >Aja
> >On 24 Sep 2015 18:15, "Nick Wedd"  wrote:
> >
> >> Thank you, Ingo, for noticing this. I did forget to post to the list
> about
> >> it.
> >>
> >> And congratulations to Abakus!
> >>
> >> Nick
> >>
> >> On 24 September 2015 at 16:58, "Ingo Althöfer" <3-hirn-ver...@gmx.de>
> >> wrote:
> >>
> >>> Hello,
> >>>
> >>> somehow there was no link here in the mailing list on
> >>> Nick Wedd's report on the Autumn slow tournament.
> >>>
> >>> http://www.weddslist.com/kgs/past/S15.2/index.html
> >>>
> >>> Congratulations to the surprise winner, Abakus,
> >>> and the co-winners!
> >>>
> >>> Ingo.
> >>> ___
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> >>> Computer-go@computer-go.org
> >>> http://computer-go.org/mailman/listinfo/computer-go
> >>
> >>
> >>
> >>
> >> --
> >> Nick Wedd  mapr...@gmail.com
> >>
> >> ___
> >> Computer-go mailing list
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> >> http://computer-go.org/mailman/listinfo/computer-go
> >>
> > inline file
> >___
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>
> >Computer-go@computer-go.org
>
> >http://computer-go.org/mailman/listinfo/computer-go
> --
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Re: [Computer-go] Report KGS Slow Tournament

[Aja Huang]

Congrats to Abakus. Does anyone know who is Abakus` author?

Aja
On 24 Sep 2015 18:15, "Nick Wedd"  wrote:

> Thank you, Ingo, for noticing this. I did forget to post to the list about
> it.
>
> And congratulations to Abakus!
>
> Nick
>
> On 24 September 2015 at 16:58, "Ingo Althöfer" <3-hirn-ver...@gmx.de>
> wrote:
>
>> Hello,
>>
>> somehow there was no link here in the mailing list on
>> Nick Wedd's report on the Autumn slow tournament.
>>
>> http://www.weddslist.com/kgs/past/S15.2/index.html
>>
>> Congratulations to the surprise winner, Abakus,
>> and the co-winners!
>>
>> Ingo.
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>
>
>
> --
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>
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Re: [Computer-go] Computer Go Turnament at EGC 2015: Call for Participation

[Aja Huang]

Hi,

Thanks. I'll attend but I'll play Go myself rather than entering a Go
program. :)

See you in Liberec,
Aja

On Tue, Jun 16, 2015 at 11:07 AM, Petr Baudis  wrote:

>   Hi!
>
> On Mon, Jun 15, 2015 at 01:21:49PM +, Josef Moudrik wrote:
> > The tournament will take place on 29th July 2015. The winner of the
> > tournament will have a chance to play against a strong professional
> player
> > In the evening. The programs will compete on equal hardware arranged by
> the
> > organizer. We can guarantee a prize budget of 600 EUR.
> >
> > I would like to invite operator/bot teams to participate in the
> tournament!
> >
> > If you are interested, the full specification is available here:
> > http://pasky.or.cz/iggsc2015/compgo_spec.html
>
>   We'd also like to remind you that the registration deadline is as soon
> as July 4.
>
>   (Officially, it is your responsibility to find an operator to run your
> program on-site if you don't attend in person.  But we may be able to
> help in a limited number of cases, roughly on a first come, first served
> basis.)
>
>   See you in Liberec,
>
> --
> Petr Baudis
> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] 25x25 experiment

[Aja Huang]

On Wed, Apr 29, 2015 at 5:54 PM, Detlef Schmicker  wrote:

>  I did not test 100k playouts, I was lower than 2k playouts in all my
> tests. No I am not sure we are stronger than Fuego, I should run on KGS...
> (possibly after the 19x19 tournament on sunday)
> We won against hirabot (1d if playing with 10k playouts on KGS I think) in
> 19x19 slow bot tournament and played about even with aya and pachi in 13x13.
>

Running your program on KGS sounds a good idea. If you want to compare with
our MCTS + 12-layer CNN, using uniform random playouts and completely
removing your hand-crafted features and rules everywhere in MCTS (except
the features fed into CNN) would be pretty close to our experimental
settings in Section 6. If your program can reach 1.5d-2d on KGS, then you
have a CNN as strong as our 12-layer one.

Aja
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Re: [Computer-go] Seki in playouts

[Aja Huang]

On Wed, Apr 15, 2015 at 12:24 PM, Igor Polyakov 
wrote:
>
> 5. But it misses this because squeezing leads to a few accidental losses
> in playouts where black plays something that white doesn't respond to
>

My two cents: just prevent the accidental losses in playouts then the
program will play B8 correctly.

Aja
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Re: [Computer-go] Fwd: Teaching Deep Convolutional Neural Networks to Play Go

[Aja Huang]

Hello Oliver,

2015-03-16 11:58 GMT+00:00 Oliver Lewis :
>
> It's impressive that the same network learned to play seven games with
> just a win/lose signal.  It's also interesting that both these teams are in
> different parts of Google. I assume they are aware of each other's work,
> but maybe Aja can confirm.
>

The authors are my colleagues at Google DeepMind as on the paper they list
DeepMind as their affiliation. Yes we are aware of each other's work.

Aja
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Re: [Computer-go] UEC Cup

[Aja Huang]

Hi Hideki,

2015-03-16 11:34 GMT+00:00 Hideki Kato :
>
> #So far, Zen often lost territory and built thickness in early stages
> and revesed in hte middle.  However, rivals are quickly getting stronger
> while top two are almost standing :-( these a few years, it's also
> getting harder to reverse.  Additionally, Nomitan plays slower (compared
> to others, such as Aya) but steadily, reversing is much harder and the
> lost territory (bottom left in attached sgf) is bigger than usuall.
> Very hard luck.
>

Zen and Crazy Stone are impressive even just being standing at 6d. But yes
we all hope that the strongest Go programs can advance to 7d even 9d. I'm
glad to see DolBaram becomes strong and GoTrend is catching up quickly.

Aja
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Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional NeuralNetworks

[Aja Huang]

2015-01-09 23:04 GMT+00:00 Darren Cook :

> Aja wrote:
> >> I hope you enjoy our work. Comments and questions are welcome.
>
> I've just been catching up on the last few weeks, and its papers. Very
> interesting :-)
>
> I think Hiroshi's questions got missed?
>

I did answer Hiroshi's questions.

http://computer-go.org/pipermail/computer-go/2014-December/007063.html

Aja
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Re: [Computer-go] Representing Komi for neural network

[Aja Huang]

2015-01-11 15:59 GMT+00:00 Detlef Schmicker :
>
> By the way:
> Todays bot tournament nicego19n (oakfoam) played with a CNN for move
> prediction.
> It was mixed into the original gamma with some quickly optimized parameter
> leading to >100ELO improvement for selfplay with 2000 playouts/move. I used
> the Clark and Storkey Network, but with no additional features (only a
> black and a white layer). I trained it on 6 kgs games and reached about
> 41% prediction rate. I have no delayed evaluation, as I evaluate no
> mini-batch but only one position taking about 1.6ms on the GTX-970. A
> little delay might happen anyway, as only one evaluation is done at once
> and other threads might go on playing while one thread is doing CNN. We
> have quite slow playouts anyway, so I had around 7 playouts/move during
> the game.
>
> If you want to get an impression, how such a bot plays, have a look at the
> games :)
>

Congrats on oakfoam's significant improvement with the CNN. The game
oakfoam beat HiroBot[1d] is very nice

http://files.gokgs.com/games/2015/1/11/HiraBot-NiceGo19N.sgf

Would you release the newest version of oakfoam and the CNN? I couldn't
find your git or svn repository at

http://oakfoam.com/#downloads

Aja
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Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

[Aja Huang]

Hi Hugh,

On Fri, Dec 26, 2014 at 9:49 AM, Hugh Perkins  wrote:

> Estimated total number of parameters
> approx = 12 layers * 128 filters * 128 previous featuremaps * 3 * 3
> filtersize
> = 1.8 million
>
> But you say 2.3 million.  It's similar, so seems feature maps are
> fully connected to lower level feature maps, but I'm not sure where
> the extra 500,000 parameters should come from?
>

You may have forgotten to include the position dependent biases. This is
how I computed the number of parameters

1st layer + 11*middle layers + final layer + 12*middle layer bias + output
bias

5*5*36*128 + 3*3*128*128*11 + 3*3*128*2 + 128*19*19*12 + 2*19*19 = 2,294,738


> 2. Symmetry
>
> Aja, you say in section 5.1 that adding symmetry does not modify the
> accuracy, neither higher or lower.  Since adding symmetry presumably
> reduces the number of weights, and therefore increases learning speed,
> why did you thus decide not to implement symmetry?


We were doing exploratory work that optimized performance not training
time, so we don't know how symmetry affects training time. In terms of
performance it seems not have an effect.

Aja
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Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional NeuralNetworks

[Aja Huang]

2014-12-21 3:02 GMT+00:00 Hiroshi Yamashita :
>
> I tried Fuego 1.1(2011, Windows version) on Intel Core i3 540,
> 2 cores 4 thread. 3.07GHz.
>

Thanks. You remind me we should write Fuego's version as "1.1.SVN" rather
than "1.1". In Clark's paper they tested against Fuego 1.1. So the reason
why our 12-layer CNN is about 300 Elo stronger than their best CNN when
playing against GnuGo but only 100+ Elo stronger when playing against
Fuego, is that we tested against different versions of Fuego.

I'm going to test our CNN against Fuego 1.1 for more precise comparison.

Aja
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Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks

[Aja Huang]

Hi Mark,

2014-12-20 19:17 GMT+00:00 Mark Wagner :

> Thanks for sharing. I'm intrigued by your strategy for integrating
> with MCTS. It's clear that latency is a challenge for integration. Do
> you have any statistics on how many searches new nodes had been
> through by the time the predictor comes back with an estimation? Did
> you try any prefetching techniques? Because the CNN will guide much of
> the search at the frontier of the tree, prefetching should be
> tractable.

Did you do any comparisons between your MCTS with and w/o CNN? That's
> the direction that many of us will be attempting over the next few
> months it seems :)


I'm glad you like the paper and would consider to attempt. :)
Thanks for the interesting suggestions.

Regards,
Aja


>
> - Mark
>
> On Sat, Dec 20, 2014 at 10:43 AM, Álvaro Begué 
> wrote:
> > If you start with a 19x19 grid and you take convolutional filters of size
> > 5x5 (as an example), you'll end up with a board of size 15x15, because a
> 5x5
> > box can be placed inside a 19x19 board in 15x15 different locations. We
> can
> > get 19x19 outputs if we allow the 5x5 box to be centered on any point,
> but
> > then you need to do multiply by values outside of the original 19x19
> board.
> > Zero-padding just means you'll use 0 as the value coming from outside the
> > board. You can either prepare a 23x23 matrix with two rows of zeros along
> > the edges, or you can just keep the 19x19 input and do your math
> carefully
> > so terms outside the board are ignored.
> >
> >
> >
> > On Sat, Dec 20, 2014 at 12:01 PM, Detlef Schmicker 
> wrote:
> >>
> >> Hi,
> >>
> >> I am still fighting with the NN slang, but why do you zero-padd the
> >> output (page 3: 4 Architecture & Training)?
> >>
> >> From all I read up to now, most are zero-padding the input to make the
> >> output fit 19x19?!
> >>
> >> Thanks for the great work
> >>
> >> Detlef
> >>
> >> Am Freitag, den 19.12.2014, 23:17 + schrieb Aja Huang:
> >> > Hi all,
> >> >
> >> >
> >> > We've just submitted our paper to ICLR. We made the draft available at
> >> > http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
> >> >
> >> >
> >> >
> >> > I hope you enjoy our work. Comments and questions are welcome.
> >> >
> >> >
> >> > Regards,
> >> > Aja
> >> > ___
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> >>
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Re: [Computer-go] Fuego 1.1 vs current Fuego

[Aja Huang]

Hi Martin,

Thanks for the explanation. I'll write Fuego runs

2 (considering early stop) * 2.2 / 3.1

roughly 14200 playouts per move on the empty board using their time limits.

Thanks,
Aja

On Sat, Dec 20, 2014 at 11:27 PM, Martin Mueller 
wrote:

> > I ran Fuego (latest svn version) on our machine (Intel(R) Xeon(R) CPU
>
> Aja,
> the 1.1 release of Fuego is from 2011 and much different from current svn
> Fuego. Current version has many different kinds of knowledge added. It is
> also much slower, maybe 5x. If you read my blog it explains this a little
> more.
>
> Martin
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Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional NeuralNetworks

[Aja Huang]

Hi Hiroshi,

2014-12-20 3:31 GMT+00:00 Hiroshi Yamashita :
>
> But it looks playing strength is similar to Clark's CNN.
>

Against GnuGo our 12-layer CNN is about 300 Elo stronger (97% winning rate
against 86% based one the same KGS games). Against Fuego using their time
setting (10 sec per move on 2 threads) our CNN scored about 30%. To
precisely compare with their results, we also ran 6 sec per move (since our
CPU is faster) and got 20-25% (against their 12%). So, our network is
clearly much stronger.


> MCTS with CNN is interesting. But CNN (0 playout) vs 1 playout is 67%
> seems small. Maybe playout is weak? I'm curious if playout uses CNN.
>

MCTS + CNN (10k playouts) scored 67% against CNN alone. Yes the playout is
still very simple.


> In page 6, "approximately 5,000 rollouts per move"
> Christopher Clark's CNN used Fuego with 10 seconds a move, 2 threads on
> an Intel i7-4702MQ. So maybe it is about 40,000 rollouts per move.
>

Thanks for the information. I'll verify that again.

Aja
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Re: [Computer-go] Move Evaluation in Go Using Deep Convolutional NeuralNetworks

[Aja Huang]

2014-12-20 11:33 GMT+00:00 Hiroshi Yamashita :
>
> I don't understand minibatch.
> Does CNN need 0.15sec for a positon, or 0.15sec for 128 positions?
>

0.15 sec for 128 positions.


>  ABCDEFGHJ
> 9.   White(O) to move.
> 8...OO   Previous Black move is H5(X)
> 7..XXXOO..
> 6.XXO.
> 5...X.
> 4.
> 3XO...
> 2OX...
> 1.
>  ABCDEFGHJ
>
> "Liberties after move" means  H7(O) is 5, F8(O) is 6.
> "Liberties" means
>  H5(X) is 3, H6(O) is 2.
> "Ladder move" means
>  G2(O), not E6(O).
>
> Is this correct?
>

Yes, all correct.


> Is "KGS rank" set 9 dan when it plays against Fuego?
>

Yes.

Aja
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Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

[Aja Huang]

2014-12-15 23:29 GMT+00:00 Petr Baudis :
>
>   Huh, aren't you?
>
>   I just played quick two games GnuGoBot39 where I tried very hard not
> to read anything at all, and had no trouble winning.  (Well, one of my
> groups had some trouble but mindless clicking saved it anyway.)


That well explains your level is far beyond GnuGo, probably at least 3k on
KGS.

That being said, Hiroshi, are you sure there was no problem in your
experiment? 6% winning rate against GnuGo on 19x19 seems too low for a
predictor of 38.8% accuracy. And yes, in the paper we will show a game that
the neural network beat Fuego (or pachi) at 100k playouts / move.

Aja
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Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

[Aja Huang]

2014-12-15 21:31 GMT+00:00 Petr Baudis :
>
>   Still, strong play makes sense for a strong predictor.  I believe I
> can also beat GNUGo >90% of time in blitz settings without doing pretty
> much *any* concious sequence reading.  So I would expect a module that's
> supposed to mirror my intuition to do the same.
>

I'm very surprised you are so confident in beating GnuGo over 90% of time
in *blitz settings*. There are even people complaining he couldn't beat
GnuGo

http://www.lifein19x19.com/forum/viewtopic.php?f=18&t=170


> > Finally, I am not a fan of NN in the MCTS architecture. The NN
> architecture imposes a high CPU burden (e.g., compared to decision trees),
> and this study didn't produce such a breakthrough in accuracy that I would
> give away performance.
>
>   ...so maybe it is MCTS that has to go!  We could be in for more
> surprises.  Don't be emotionally attached to your groups.
>

Fair enough. :)

Aja
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Re: [Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

[Aja Huang]

Chris Maddison also produced very good (in fact much better) results using
a deep convolutional network during his internship at Google. Currently
waiting for publication approval, I will post the paper once it is passed.

Aja

On Mon, Dec 15, 2014 at 2:59 PM, Erik van der Werf  wrote:
>
> Thanks for posting this Hiroshi!
>
> Nice to see this neural network revival. It is mostly old ideas, and it is
> not really surprising to me, but with modern compute power everyone can now
> see that it works really well. BTW for some related work (not cited),
> people might be interested to read up on the 90s work of Stoutamire,
> Enderton, Schraudolph and Enzenberger.
>
> Comparing results to old publications is a bit tricky. For example, the
> things I did in 2001/2002 are reported to achieve around 25% prediction
> accuracy, which at the time seemed good but is now considered unimpressive.
> However, in hindsight, an important reason for that number was time
> pressure and lack of compute power, which is not really related to anything
> fundamental. Nowadays using nearly the same training mechanism, but with
> more data and more capacity to learn (i.e., a bigger network), I also get
> pro-results around 40%. In case you're interested, this paper
> http://arxiv.org/pdf/1108.4220.pdf by Thomas Wolf has a figure with more
> recent results (the latest version of Steenvreter is still a little bit
> better though).
>
> Another problem with comparing results is the difficulty to obtain
> independent test data. I don't think that was done optimally in this case.
> The problem is that, especially for amateur games, there are a lot of
> people memorizing and repeating the popular sequences. Also, if you're not
> careful, it is quite easy to get duplicate games in you dataset (I've had
> cases where one game was annotated in chinese, and the other (duplicate) in
> English, or where the board was simply rotated). My solution around this
> was to always test on games from the most recent pro-tournaments, for which
> I was certain they could not yet be in the training database. However, even
> that may not be perfect, because also pro's play popular joseki, which
> means there will at least be lots of duplicate opening positions.
>
> I'm not surprised these systems now work very well as stand alone players
> against weak opponents. Some years ago David and Thore's move predictors
> managed to beat me once in a 9-stones handicap game, which indicates that
> also their system was already stronger than GNU Go. Further, the version of
> Steenvreter in my Android app at its lowest level is mostly just a move
> predictor, yet it still wins well over 80% of its games.
>
> In my experience, when the strength difference is big, and the game is
> even, it is usually enough for the strong player to only play good shape
> moves. The move predictors only break down in complex tactical situations
> where some form of look-ahead is critical, and the typical shape-related
> proverbs provide wrong answers.
>
> Erik
>
> On Mon, Dec 15, 2014 at 12:53 AM, Hiroshi Yamashita 
> wrote:
>>
>> Hi,
>>
>> This paper looks very cool.
>>
>> Teaching Deep Convolutional Neural Networks to Play Go
>> http://arxiv.org/pdf/1412.3409v1.pdf
>>
>> Thier move prediction got 91% winrate against GNU Go and 14%
>> against Fuego in 19x19.
>>
>> Regards,
>> Hiroshi Yamashita
>>
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[Computer-go] Please keep the old mailing list up for a while

[Aja Huang]

Thanks Michael and Petr for setting up computer-go at computer-go.org
.

Please keep the old mailing list computer-go at dvandva.org
 up for a while.
We will investigate the issue of Gmail as soon as possible. Thanks.

Aja
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