date:20160201

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Hideki Kato

Ingo Althofer: 
: 
>Hi Hideki,
>
>first of all congrats to the nice performance of Zen over the weekend!
>
>> Ingo and all,
>> Why you care AlphaGo and DCNN so much?  
>
>I can speak only for myself. DCNNs may be not only applied to
>achieve better playing strength. One may use them to create
>playing styles, or bots for go variants.
>
>One of my favorites is robot frisbee go. 
>http://www.althofer.de/robot-play/frisbee-robot-go.jpg
>Perhaps one can teach robots with DCNN to throw the disks better.
>
>And my expectation is: During 2016 we will see many more fantastic
>applications of DCNN, not only in Go. (Olivier had made a similar
>remark already.)

Agree but one criticism.  If such great DCNN applications all 
need huge machine power like AlphaGo (upon execution, not 
training), then the technology is hard to apply to many areas, 
autos and robots, for examples.  Are DCNN chips the only way to 
reduce computational cost?  I don't forecast other possibilities.  
Much more economical methods should be developed anyway.
#Our brain consumes less than 100 watt.

Hideki

>Ingo.
>
>PS. Dietmar Wolz, my partner in space trajectory design, just told me
>that in his company they started woth deep learning...
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-- 
Hideki Kato 
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Rainer Rosenthal


~~
Robert: "Hey, AI, you should provide explanations!"
AI: "Why?"
~~

Cheers,
Rainer

Date: Mon, 1 Feb 2016 08:15:12 -0600
From: "Jim O'Flaherty" 
To: computer-go@computer-go.org
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural
Networks and Tree Search
Message-ID:

Content-Type: text/plain; charset="utf-8"

Robert,

I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
systems to/into a Go engine.


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[Computer-go] Conference CG2016 in Leiden: NN in Games

2016-02-01 Thread Ingo Althöfer

Hello everybody,

some weeks ago I had given a hint already on the conference
CG2016 (CG standing for "Computer and Games"), to take place in
Leiden (NL) on June 29 - July 01.

https://cg2016leiden.wordpress.com/

The deadline for papers has been prolonged already to February 11.

In view of the current DNN-explosion there are plans to have another
prolongement for paper submissions in the field of Neural Nets - and
also a special workshop for "Neural Nets in Games". 

So, start your research and contribute a paper...
More information will be given, when available.

Ingo (Vize President of the ICGA).
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Ingo Althöfer

Hi Hideki,

you put it wonderfully into two lines:

**
**
******
***  Much more economical methods should be developed anyway.  ***
*** #Our brain consumes less than 100 watt.***
******
**
**


Hopefully the box remains formatted nicely ;-)

Ingo.
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Álvaro Begué

Aja,

I read the paper with great interest. [Insert appropriate praises here.]

I am trying to understand the part where you use reinforcement learning to
improve upon the CNN trained by imitating humans. One thing that is not
explained is how to determine that a game is over, particularly when a
player is simply a CNN that has a probability distribution as its output.
Do you play until every point is either a suicide or looks like an eye? Do
you do anything to make sure you don't play in a seki?

I am sure you are a busy man these days, so please answer only when you
have time.

Thanks!
Álvaro.

On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  wrote:

> 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
>
> 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] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Brian Sheppard

You play until neither player wishes to make a move. The players are willing to 
move on any point that is not self-atari, and they are willing to make 
self-atari plays if capture would result in a Nakade 
(http://senseis.xmp.net/?Nakade)

 

This correctly plays seki. 

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Brian Cloutier
Sent: Monday, February 1, 2016 5:02 PM
To: computer-go 
Subject: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

 

> One thing that is not explained is how to determine that a game is over

You'll find that very little of the literature explicitly covers this. When I 
asked this question I had to search a lot of papers on MCTS which mentioned 
"terminal states" before finding one which defined them.

Let me see if I can find the actual paper, but they defined it as a position 
where there are no more legal moves. You're right though, that ignores sekis, 
which makes me think I'm remembering wrong.

On Mon, Feb 1, 2016, 13:45 Álvaro Begué  > wrote:

 

Aja,

 

I read the paper with great interest. [Insert appropriate praises here.]

 

I am trying to understand the part where you use reinforcement learning to 
improve upon the CNN trained by imitating humans. One thing that is not 
explained is how to determine that a game is over, particularly when a player 
is simply a CNN that has a probability distribution as its output. Do you play 
until every point is either a suicide or looks like an eye? Do you do anything 
to make sure you don't play in a seki?

 

I am sure you are a busy man these days, so please answer only when you have 
time.

 

Thanks!

Álvaro.

 

 

 

On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang  > wrote:

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

 

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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[Computer-go] DCNN can solve semeai?

2016-02-01 Thread Hiroshi Yamashita


Hi,

I made DCNN, and tried whether DCNN can understand semeai.

1. try one playout that always select DCNN highest probablity move.
2. try 100 playouts that select moves from DCNN probability.
  (one playout takes 4 seconds.)

Result is DCNN does not understand semeai. It can play semeai like moves,
but far from perfect. Maybe need more difficult feature?

  DCNN highestDCNN 100 playouts Aya playout
problem1   0 53   20
problem2   0 54   85
problem3 100 91   70
problem4 100 66   66
problem5   0 51   62
problem6   0 45   67
problem7 100 95   90
problem8   0  9   90
problem9   0 39   50

Average   33 56   67

100 is correct. 0 is wrong.
53 means DCNN playout is correct 53 times out of 100 playouts.


DCNN is 
 12 layers, 128 filters. (5x5_128, 3x3_128 x10, 3x3_1)

 It predicts next 3 moves, like Facebook paper.
 Test accuracy is next_1 49%, next_2 27%, next_3 16%.
 http://www.yss-aya.com/20160123_3steps.png
 Feature are
   liberty black and white. 1,2,3,4>=
   stones black and white. 1,2,3,4>=
   previous move 1,2,3,4,5
   previous ko 1,2,3
   CFG Distance 1,2,3,4,5>=
   string life and death by search. dead, killed next, kill move,
life move. 8 planes
   group life and death. It is from Aya's classic evaluation(KGS 8k).
6 planes
   territory. black and white.
   half eye. black and white.
   recapture soon. (if black play here, recapture soon). black and white
 All 49 channels.
 Learning games are from GoGoD, 78000 games.
 This DCNN runs on CGOS by DCNN_Aya_i49_a49, no search. GTX 980.
 Its winrate 40% against Pachi 100k.
 AlphaGo DCNN(RL) winrate is 85% against Pachi 100k.
 So this DCNN is weaker than AlphaGo(RL) by 370 Elo,
 and weaker than AlphaGo(SL) by 129 Elo.


Problem8, W lives. 4 libs vs 5 libs (top left)
Problem9, W lives. 5 libs vs 6 libs (bottom left)

19.O.X.XO.OX.   DCNN answered badly these two problem.
18OXO.XX.
17.OO.X.O.O..   Left Top is 4 libs vs 5 libs
16OO.X...O...   White(O) must live
15.XX   
14X...O..   DCNN answer is White(O) lives 9%

13...   DCNN best move playout also fails.
12...
11O..
10...
9O..
8XX.X...
7.OX.X..O...
6O.X
5OX..O..  Left bottom is 5 libs vs 6 libs.
4OX.O...  White(O) must live
3OX..O..
2XO.XOX.  DCNN answer is White(O) lives 39%
1.OOXOX.  DCNN best move playout also fails.
 ABCDEFGHJKLMNOPQRST

(;GM[1]SZ[19]KM[0.5]RU[Chinese]AB[da][fa][ja][bb][cb][db][eb][fb][ib]
[jb][ic][dd][ed][fd][gd][hd][be][ce][af][al][bl][dl][cm][em][cn][fo]
[fp][aq][bq][cq][dq][fq][ar][dr][fr][ds][fs]AW[ba][ga][ia][ab][gb]
[bc][cc][dc][ec][fc][gc][oc][qc][ad][bd][pd][qf][qi][qk][bm][pm][an]
[ao][qo][ap][bp][cp][dp][ep][pp][eq][qq][br][er][bs][cs][es])


Problem1, B lives. 3 libs vs 5 libs. share 2 libs. (top)
Problem2, B lives. (right bottom)

(;GM[1]SZ[19]KM[0.5]TM[]RU[Chinese];B[pe];W[qp];B[dq];W[cd];B[op];
W[do];B[fq];W[cq];B[cp];W[dp];B[bq];W[cr];B[ep];W[dr];B[eq];W[co];
B[bp];W[dk];B[bo];W[bn];B[cn];W[dn];B[cm];W[dm];B[cl];W[dl];B[pm];
W[oo];B[po];W[pp];B[oq];W[no];B[qo];W[ro];B[rn];W[rp];B[qm];W[mq];
B[qr];W[rr];B[qq];W[rq];B[nr];W[mr];B[rs];W[pq];B[pr];W[qs];B[ps];
W[sr];B[ns];W[nm];B[so];W[pi];B[sp];W[ed];B[jd];W[ld];B[gd];W[lf];
B[nd];W[jf];B[ec];W[dc];B[fc];W[gf];B[he];W[ig];B[fn];W[fo];B[eo];
W[ck];B[bm];W[fm];B[gn];W[gm];B[hn];W[hm];B[in];W[im];B[jn];W[jq];
B[jm];W[hq];B[jl];W[en];B[gp];W[pg];B[qf];W[qc];B[oc];W[oe];B[pc];
W[pd];B[qd];W[od];B[mc];W[rd];B[qe];W[rc];B[rh];W[qg];B[rg];W[lc];
B[qb];W[rb];B[pb];W[re];B[rf];W[qk];B[ra];W[ne];B[se];W[md];B[nc];
W[jb];B[ic];W[ib];B[hb];W[hc];B[hd];W[ha];B[gb];W[ie];B[fe];W[id];
B[jc];W[kb];B[ff];W[fg];B[eg];W[dg];B[df];W[eh];B[de];W[dd];B[db];
W[cb];B[ef];W[cg];B[be];W[eb];B[fh];W[gg];B[dh];W[ch];B[ei];W[ci];
B[hi];W[ij];B[hj];W[fj];B[fi];W[ik];B[ii];W[ji];B[jh];W[ih];B[kh];
W[ki];B[lh];W[li];B[mh];W[gh];B[gi];W[kf];B[mi];W[jj];B[hf];W[if];
B[fk];W[hk];B[bf];W[bc];B[ej];W[bg];B[of];W[nf];B[og];W[mj];B[oh];
W[nj];B[mb];W[lb];B[la];W[pf];B[ph];W[gk];B[gj];W[ek];B[mg];W[mf];
B[ia];W[ge];B[fd];W[fb];B[ga];W[di];B[eh];W[hh];B[kd];W[le];B[kg];
W[ng];B[nh];W[qi];B[qh];W[ja];B[gc];W[bd];B[lg];W[lm];B[jg];W[ms];
B[ma];W[fl];B[ha];W[af];B[hc];W[ae];B[je];W[da];B[ka];W[ce];B[kc])

Problem3, W lives. 3 libs vs 4 libs. (top left)
Problem4, B dead.(right bottom)

(;GM[1]SZ[19]KM[7.5]RU[Chinese];B[qd];W[pp];B[dd];W[dp];B[qn];W[nq];

Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

2016-02-01 Thread Michael Markefka

I agree.

It might be interesting to set this up a while after the Lee Sedol
matches if Ke Jie still holds the #1 spot at at that time. After
beating the best player of the past ten years, beating the currently
best player would in a way complete AlphaGo's victory over current
human Go ability.

On Mon, Feb 1, 2016 at 4:04 AM, Marc Landgraf  wrote:
> Why would they water down their Lee Sedol game by announcing another
> game before their big game has even happened? No matter if that game
> would be before or after.
> Sounds like an awful PR strategy.
>
> 2016-02-01 2:51 GMT+01:00 uurtamo . :
>> It might even be interesting if it took place *before* the lee sedol match.
>>
>> s.
>>
>> On Jan 31, 2016 5:09 PM, "Chaohao Pan"  wrote:
>>>
>>> Just in case that no one knows it. Ke Jie has publicly announced that he
>>> is willing to play against AlphaGo, even without any prize money. Since Ke
>>> Jie is absolutely the current No.1, it would be a good choice to have
>>> another match with Ke Jie, time permitting, no matter AlphaGo wins or loses
>>> against Lee Sedol,.
>>>
>>> Chaohao
>>>
>>> 2016-01-31 13:34 GMT-08:00 John Tromp :

 > You must be kidding about Lee Sedol.
 > ...
 > So he was by far the biggest fish Google could ever catch for that
 > game, for Go insiders as well as for people outside the Go scene.

 Well said, Marc.

 In terms of name recognition and domination in the past decade,
 who else but Lee Sedol should be picked as the "Kasparov of Go"
 in the ultimate Man vs Machine match?

 -John
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>>>
>>>
>>>
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>>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Darren Cook

> someone cracked Go right before that started. Then I'd have plenty of
> time to pick a new research topic." It looks like AlphaGo has 
> provided.

It seems [1] the smart money might be on Lee Sedol:

1. Ke Jie (world champ) – limited strength…but still amazing… Less than
5% chance against Lee Sedol now. But as it can go stronger, who knows
its future…
2. Mi Yuting (world champ) – appears to be a ‘chong-duan-shao-nian (kids
on the path to pros)’, ~high-level amateur.
3, Li Jie (former national team player) – appears to be pro-level. one
of the games is almost perfect (for AlphaGo)


On the other hand, AlphaGo got its jump in level very quickly (*), so it
is hard to know if they just got lucky (i.e. with ideas things working
first time) or if there is still some significant tweaking possible in
these 5 months of extra development (October 2015 to March 2016).

Have the informal game SGFs been uploaded anywhere? I noticed (Extended
Data Table 1) they were played *after* the official game each day, so
the poor pro should have been tired, but instead he won 2 of the 5 (day
1 and day 5). Was this just due to the short time limits, or did Fan Hui
play a different style (e.g. more aggressively)?


Darren



[1]: Comment by xli199 at
http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/

[2]: When did DeepMind start working on go? I suspect it might only
after have been after the video games project started to wound down,
which would've Feb 2015? If so, that is only 6-8 months (albeit with a
fairly large team).
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Michael Markefka

On Mon, Feb 1, 2016 at 10:19 AM, Darren Cook  wrote:
> It seems [1] the smart money might be on Lee Sedol:

In the DeepMind press conferences (
https://www.youtube.com/watch?v=yR017hmUSC4 -
https://www.youtube.com/watch?v=_r3yF4lV0wk ) Demis Hassabis stated,
that he was quietly confident.

I assume that means they've got a version up and running that at least
matches Lee Sedol's Elo rating, perhaps even slightly exceeding it.
They might be wary of the engine displaying some idiosyncracy they
haven't picked up on yet, which Sedol might notice and then exploit.
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Re: [Computer-go] Zen19X achieved stable KGS 7d

2016-02-01 Thread Michael Markefka

On Mon, Feb 1, 2016 at 1:44 PM, Hideki Kato  wrote:
> I was, btw, really surprised when Zen beat fj with two stones
> handi.
> http://files.gokgs.com/games/2016/1/31/Zen19X-fj.sgf
>
> Hideki

On the DGoB forums fj stated, possibly in jest, that this was an even
game, as he had had a glass of wine for every stone of handicap there.
Seems like he wasn't in the most competitive of moods. Still a good
game though. :)
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Re: [Computer-go] Zen19X achieved stable KGS 7d

2016-02-01 Thread Petr Baudis

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=2016=1=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] AlphaGo and the Standard Mistake in Research and Journalism

2016-02-01 Thread Olivier Teytaud

>
>
> I am pretty sure that such an implicit expression exists: it is << the
>> number of etc etc
>>
>
> We do not speak of just the definition of what kind of number to find, but
> of the construction of finding the number (or already of a compression of
> its explicit digits).


It's hard to come up with a formal definition of "implicit expression"
which excludes the definition itself :-)  maybe something like a fast
algorithm for computing a given digit (this exists for pi
https://www.math.hmc.edu/funfacts/ffiles/20010.5.shtml ).

(Well, it's fun, but I would not spend days on this :-) )
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Thomas Wolf


The next type of event could be a new 'Pair Go'
Where a human and a program make up a pair, like Mark Zuckerberg and his 
facebook
program against a Google VP and alphaGo. :-)

Thomas

On Mon, 1 Feb 2016, John Tromp wrote:


For those of you who missed it, chess grandmaster Hikaru Nakamura,
rated 2787, recently played a match against the world's top chess program
Komodo, rated 3368. Each of the 4 games used a different kind of handicap:

Pawn and Move Odds
Pawn Odds
Exchange Odds
4-Move Odds

As you can see, handicaps in chess are no easy matter:-(
When AlphaGo surpasses the top human professionals we may see such
handicap challenges in the future. One may wonder if we'll ever see a
computer giving 4 handicap to a professional...

So how did Nakamura fare? See for yourself at

https://www.chess.com/news/komodo-beats-nakamura-in-final-battle-1331

regards,
-John
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Jim O'Flaherty

Robert,

I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
systems to/into a Go engine. Which language would be the one to use;
English, Chinese, Japanese, etc? As abstraction goes deeper, the nuance of
each human language diverges from the others (due to the way the human
brain is just a fractal based analogy making engine). The scare resource is
human mind power producing advances on the main goal making a superior AI
to what already exists. As the linguistic pathway hasn't emerged in Chess
in the last decade, then I find it considerably less likely it will end up
emerging for Go...unless you are, of course, suggesting that is something
you are taking up. :)

The AI world is changing to make explaining computation cognition to humans
less necessary, or even desirable. Why bound the solution space to only
what cognitively linguistically limited humans can imagine and/or consider?
And given even one AI team is thinking this way, the nature of competition
will drive other competing teams to similar motivation(s). Welcome to
"memetic evolution in action". Kind of makes those of us in the nearby
human cognitive domains just a wee bit more nervous about what is rapidly
approaching as human cognition automateable. For example, books about
josekis could be rendered far less valuable if/when AlphaGo and some other
AI competitor more strongly influenced by josekis pushes AlphaGo into new
spaces which involve much longer resolution horizons than humans used for
those that exist now.

No matter what, the future sure does sound very exciting now that Alpha Go
has broken the Go AI ceiling. I cannot WAIT to see the results of the event
against Lee Sedol.

Congratulations, Alpha Go team and Aja!

Jim

On Mon, Feb 1, 2016 at 12:50 AM, Robert Jasiek  wrote:

> On 01.02.2016 07:30, Petri Pitkanen wrote:
>
>> Explaining why the move is good in human terms is useless goal. Good chess
>> programs cannot do it nor it is meaningful. As the humans and computers
>> have vastly different approach to selecting a move then  by the definition
>> have reasons for moves. As an example your second item 'long-term aji',
>> For
>> human an important short cut but computer a mere result for seeing far
>> enough in the future or combining several features of postion into
>> non-linear/linear computation.
>>
>
> Such is not "useless" but requires additional research or implementation.
>
>
> --
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Petr Baudis

  Hi!

On Mon, Feb 01, 2016 at 01:38:28PM +, Aja Huang wrote:
> On Mon, Feb 1, 2016 at 11:38 AM, Petr Baudis  wrote:
> >
> > That's right, but unless I've overlooked something, I didn't see Fan Hui
> > create any complicated fight, there wasn't any semeai or complex
> > life (besides the by-the-book oonadare).  This, coupled with the
> > fact that there is no new mechanism to deal with these (unless the value
> > network has truly astonishing generalization capacity, but it just
> > remembering common tsumego and joseki shapes is imho a simpler
> > explanation), leads me to believe that it remains a weakness.
> >
> 
> If you check Myungwan Kim 9p's comments in the video, in the 4th game there
> was a semeai that AlphaGo read out at top side. See the game at
> 
> http://britgo.org/deepmind2016/summary

  (It's at ~1:33:00+ https://www.youtube.com/watch?v=NHRHUHW6HQE)

  Well, there was a potential semeai, but did AlphaGo read it out?
I don't know, you probably do. :-)

> Unfortunately before the Lee match I'm not allowed to answer some of the
> interesting questions raised in this thread, or mention how strong is
> AlphaGo now. But for now what I can say is that in the nature paper (about
> 5 months ago) AlphaGo reached nearly 100% win rate against the latest
> commercial versions of Crazy Stone and Zen, and AlphaGo still did well even
> on 4 handicap stones, suggesting AlphaGo may do much better in tactical
> situations than Crazy Stone and Zen.

  But CrazyStone and Zen are also pretty bad at semeai and tsumego, it's
a bit of a self-play problem; when playing against MCTS programs, some
mistakes aren't revealed.

  (I guess that you probably played tens of games against AlphaGo
yourself, so you'll have a pretty good idea about its capabilities.
I just can't imagine how will the value network count and pick liberties
or tsumego sequence combinations; it might just have more memory
capacity than we'd imagine.)

> I understand you bet on Lee but I hope you will enjoy watching the match. :)

  I certainly will!  And in my heart, maybe I root for AlphaGo too :)

-- 
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If you have good ideas, good data and fast computers,
you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] AlphaGo and the Standard Mistake in Research and Journalism

2016-02-01 Thread Olivier Teytaud

>
>
> How do you know that an implicit expression (of length smaller than 10^80)
> of the number does not exist? :)
>

I am pretty sure that such an implicit expression exists: it is << the
number of etc etc >> (formalized for your favorite set of rules :-) ).




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Re: [Computer-go] The Game AI Forum is back

2016-02-01 Thread Richard Lorentz

Thank you very much. Personally I find it much easier to keep up with 
and follow topics in this kind of format. Perhaps we can encourage 
people who post on the mailing list to post on your Forum instead/too?


-Richard

P.S. Happy New Year!


On 01/01/2016 12:56 AM, Rémi Coulom wrote:

Hi,

I had created the Game Programming Forum a few years ago. I decided to 
put it online again, at a new URL: 
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.game-2Dai-2Dforum.org_=CwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=k7ciuFNHc56S8sYi-wsSC3UjQqtMtd8vZwuQLSElt0U=Qe20Mv7Kkbe7tMuviF1S9NBlJEB_lWpPF2m-yVhiEJY= 


Maybe some of you will be interested to participate there.

Rémi
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Petr Baudis

On Mon, Feb 01, 2016 at 12:24:21PM +0100, Olivier Teytaud wrote:
> If AlphaGo had lost at least one game, I'd understand how people can have
> an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
> have an upper bound on his level. After all, AlphaGo might just have played
> well enough for crushing Fan Hui, and a weak move while the position is
> still in favor of AlphaGo is not really a weak move (at least in a
> game-theoretic point of view...).

That's right, but unless I've overlooked something, I didn't see Fan Hui
create any complicated fight, there wasn't any semeai or complex
life (besides the by-the-book oonadare).  This, coupled with the
fact that there is no new mechanism to deal with these (unless the value
network has truly astonishing generalization capacity, but it just
remembering common tsumego and joseki shapes is imho a simpler
explanation), leads me to believe that it remains a weakness.

Of course there are other possibilities, like AlphaGo always steering
the game in a calmer direction due to some emergent property.  But
sometimes, you just have to go for the fight, don't you?

Petr Baudis
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Hideki Kato


Olivier Teytaud:

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Petr Baudis

  Hi!

On Mon, Feb 01, 2016 at 09:19:56AM +, Darren Cook wrote:
> > someone cracked Go right before that started. Then I'd have plenty of
> > time to pick a new research topic." It looks like AlphaGo has 
> > provided.
> 
> It seems [1] the smart money might be on Lee Sedol:
> 
> 1. Ke Jie (world champ) – limited strength…but still amazing… Less than
> 5% chance against Lee Sedol now. But as it can go stronger, who knows
> its future…
> 2. Mi Yuting (world champ) – appears to be a ‘chong-duan-shao-nian (kids
> on the path to pros)’, ~high-level amateur.
> 3, Li Jie (former national team player) – appears to be pro-level. one
> of the games is almost perfect (for AlphaGo)
> 
> 
> On the other hand, AlphaGo got its jump in level very quickly (*), so it
> is hard to know if they just got lucky (i.e. with ideas things working
> first time) or if there is still some significant tweaking possible in
> these 5 months of extra development (October 2015 to March 2016).

  AlphaGo's achievement is impressive, but I'll bet on Lee Sedol
any time if he gets some people to explain the weaknesses of computers
and does some serious research.

  AlphaGo didn't seem to solve the fundamental reading problems of
MCTS, just compensated with great intuition that can also remember
things like corner life shapes.  But if Lee Sedol gets the game to
a confusing fight with a long semeai or multiple unusual life
shapes, I'd say based on what I know on AlphaGo that it'll collapse just
as current programs would.  And, well, Lee Sedol is rather famous for
his fighting style.  :)

  Unless of course AlphaGo did achieve yet another fundamental
breakthrough since October, but I suspect it'll be a long process yet.
For the same reason, I think strong players that'd play against AlphaGo
would "learn to beat it" just as you see with weaker players+bots on
KGS.

  I wonder how AlphaGo would react to an unexpected deviation from a
joseki that involves a corner semeai.

> [1]: Comment by xli199 at
> http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/
> 
> [2]: When did DeepMind start working on go? I suspect it might only
> after have been after the video games project started to wound down,
> which would've Feb 2015? If so, that is only 6-8 months (albeit with a
> fairly large team).

  Remember the two first authors of the paper:

  * David Silver - his most cited paper is "Combining online and offline
knowledge in UCT", the 2007 paper that introduced RAVE

  * Aja Huang - the author of Erica, among many other things

  So this isn't a blue sky research at all, and I think they had Go in
crosshairs for most of the company's existence.  I don't know the
details of how DeepMind operates, but I'd imagine the company works
on multiple things at once. :-)

-- 
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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

2016-02-01 Thread Olivier Teytaud

If AlphaGo had lost at least one game, I'd understand how people can have
an upper bound on its level, but with 5-0 (except for Blitz) it's hard to
have an upper bound on his level. After all, AlphaGo might just have played
well enough for crushing Fan Hui, and a weak move while the position is
still in favor of AlphaGo is not really a weak move (at least in a
game-theoretic point of view...).



On Mon, Feb 1, 2016 at 12:12 PM, Petr Baudis  wrote:

>   Hi!
>
> On Mon, Feb 01, 2016 at 09:19:56AM +, Darren Cook wrote:
> > > someone cracked Go right before that started. Then I'd have plenty of
> > > time to pick a new research topic." It looks like AlphaGo has
> > > provided.
> >
> > It seems [1] the smart money might be on Lee Sedol:
> >
> > 1. Ke Jie (world champ) – limited strength…but still amazing… Less than
> > 5% chance against Lee Sedol now. But as it can go stronger, who knows
> > its future…
> > 2. Mi Yuting (world champ) – appears to be a ‘chong-duan-shao-nian (kids
> > on the path to pros)’, ~high-level amateur.
> > 3, Li Jie (former national team player) – appears to be pro-level. one
> > of the games is almost perfect (for AlphaGo)
> >
> >
> > On the other hand, AlphaGo got its jump in level very quickly (*), so it
> > is hard to know if they just got lucky (i.e. with ideas things working
> > first time) or if there is still some significant tweaking possible in
> > these 5 months of extra development (October 2015 to March 2016).
>
>   AlphaGo's achievement is impressive, but I'll bet on Lee Sedol
> any time if he gets some people to explain the weaknesses of computers
> and does some serious research.
>
>   AlphaGo didn't seem to solve the fundamental reading problems of
> MCTS, just compensated with great intuition that can also remember
> things like corner life shapes.  But if Lee Sedol gets the game to
> a confusing fight with a long semeai or multiple unusual life
> shapes, I'd say based on what I know on AlphaGo that it'll collapse just
> as current programs would.  And, well, Lee Sedol is rather famous for
> his fighting style.  :)
>
>   Unless of course AlphaGo did achieve yet another fundamental
> breakthrough since October, but I suspect it'll be a long process yet.
> For the same reason, I think strong players that'd play against AlphaGo
> would "learn to beat it" just as you see with weaker players+bots on
> KGS.
>
>   I wonder how AlphaGo would react to an unexpected deviation from a
> joseki that involves a corner semeai.
>
> > [1]: Comment by xli199 at
> >
> http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/
> >
> > [2]: When did DeepMind start working on go? I suspect it might only
> > after have been after the video games project started to wound down,
> > which would've Feb 2015? If so, that is only 6-8 months (albeit with a
> > fairly large team).
>
>   Remember the two first authors of the paper:
>
>   * David Silver - his most cited paper is "Combining online and offline
> knowledge in UCT", the 2007 paper that introduced RAVE
>
>   * Aja Huang - the author of Erica, among many other things
>
>   So this isn't a blue sky research at all, and I think they had Go in
> crosshairs for most of the company's existence.  I don't know the
> details of how DeepMind operates, but I'd imagine the company works
> on multiple things at once. :-)
>
> --
> 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] Zen19X achieved stable KGS 7d

2016-02-01 Thread 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=2016=1=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. :)
>
> --
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> If you have good ideas, good data and fast computers,
> you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] Zen19X achieved stable KGS 7d

2016-02-01 Thread Hideki Kato

Aja Huang:

Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Olivier Teytaud

Ok, it's not blitz according to http://senseis.xmp.net/?BlitzGames
(limit at 10s/move for Blitz). But really shorter time settings.

I've seen (as you all) many posts guessing that AlphaGo will lose, but I
find
that hard to know. If Fan Hui had won one game, I would say that AlphaGo is
not ready for Lee Sedol, but with 5-0...

(incidentally, there is one great piece of news for machine learning
people: people in industry are much more interested than before for letting
us try our deep learning algorithms on their data and that's good for the
world :-) )
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread John Tromp

For those of you who missed it, chess grandmaster Hikaru Nakamura,
rated 2787, recently played a match against the world's top chess program
Komodo, rated 3368. Each of the 4 games used a different kind of handicap:

Pawn and Move Odds
Pawn Odds
Exchange Odds
4-Move Odds

As you can see, handicaps in chess are no easy matter:-(
When AlphaGo surpasses the top human professionals we may see such
handicap challenges in the future. One may wonder if we'll ever see a
computer giving 4 handicap to a professional...

So how did Nakamura fare? See for yourself at

https://www.chess.com/news/komodo-beats-nakamura-in-final-battle-1331

regards,
-John
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Re: [Computer-go] DCNN can solve semeai?

2016-02-01 Thread Hiroshi Yamashita


Hi Detlef,

My study heavily depends on your information. Especially Oakfoam code,
lenet.prototxt and generate_sample_data_leveldb.py was helpful. Thanks!


Quite interesting that you do not reach the prediction rate 57% from
the facebook paper by far too! I have the same experience with the


I'm trying 12 layers 256 filters, but it is around 49.8%.
I think 57% is maybe from KGS games.


Did you strip the games before 1800AD, as mentioned in the FB paper? I
did not do it and was thinking my training is not ok, but as you have
the same result probably this is the only difference?!


I also did not use before 1800AD. And don't use hadicap games.
Training positions are 15693570 from 76000 games.
Test positions are   445693 from  2156 games.
All games are shuffled in advance. Each position is randomly rotated.
And memorizing 24000 positions, then shuffle and store to LebelDB.
At first I did not shuffle games. Then accuracy is down each 61000
iteration (one epoch, 256 mini-batch).
http://www.yss-aya.com/20160108.png
It means DCNN understands easily the difference 1800AD games and
2015AD games. I was surprised DCNN's ability. And maybe 1800AD games
are also not good for training?

Regards,
Hiroshi Yamashita

- Original Message - 
From: "Detlef Schmicker" 

To: 
Sent: Tuesday, February 02, 2016 3:15 PM
Subject: Re: [Computer-go] DCNN can solve semeai?


Thanks a lot for sharing this.

Quite interesting that you do not reach the prediction rate 57% from
the facebook paper by far too! I have the same experience with the
GoGoD database. My numbers are nearly the same as yours 49% :) my net
is quite simelar, but I use 7,5,5,3,3, with 12 layers in total.

Did you strip the games before 1800AD, as mentioned in the FB paper? I
did not do it and was thinking my training is not ok, but as you have
the same result probably this is the only difference?!

Best regards,

Detlef


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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread George Dahl

If anything, the other great DCNN applications predate the application of
these methods to Go. Deep neural nets (convnets and other types) have been
successfully applied in computer vision, robotics, speech recognition,
machine translation, natural language processing, and hosts of other areas.
The first paragraph of the TensorFlow whitepaper (
http://download.tensorflow.org/paper/whitepaper2015.pdf) even mentions
dozens at Alphabet specifically.

Of course the future will hold even more exciting applications, but these
techniques have been proven in many important problems long before they had
success in Go and they are used by many different companies and research
groups. Many example applications from the literature or at various
companies used models trained on a single machine with GPUs.

On Mon, Feb 1, 2016 at 12:00 PM, Hideki Kato  wrote:

> Ingo Althofer:
>  >:
> >Hi Hideki,
> >
> >first of all congrats to the nice performance of Zen over the weekend!
> >
> >> Ingo and all,
> >> Why you care AlphaGo and DCNN so much?
> >
> >I can speak only for myself. DCNNs may be not only applied to
> >achieve better playing strength. One may use them to create
> >playing styles, or bots for go variants.
> >
> >One of my favorites is robot frisbee go.
> >http://www.althofer.de/robot-play/frisbee-robot-go.jpg
> >Perhaps one can teach robots with DCNN to throw the disks better.
> >
> >And my expectation is: During 2016 we will see many more fantastic
> >applications of DCNN, not only in Go. (Olivier had made a similar
> >remark already.)
>
> Agree but one criticism.  If such great DCNN applications all
> need huge machine power like AlphaGo (upon execution, not
> training), then the technology is hard to apply to many areas,
> autos and robots, for examples.  Are DCNN chips the only way to
> reduce computational cost?  I don't forecast other possibilities.
> Much more economical methods should be developed anyway.
> #Our brain consumes less than 100 watt.
>
> Hideki
>
> >Ingo.
> >
> >PS. Dietmar Wolz, my partner in space trajectory design, just told me
> >that in his company they started woth deep learning...
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Re: [Computer-go] DCNN can solve semeai?

2016-02-01 Thread Detlef Schmicker

-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

Thanks a lot for sharing this.

Quite interesting that you do not reach the prediction rate 57% from
the facebook paper by far too! I have the same experience with the
GoGoD database. My numbers are nearly the same as yours 49% :) my net
is quite simelar, but I use 7,5,5,3,3, with 12 layers in total.

Did you strip the games before 1800AD, as mentioned in the FB paper? I
did not do it and was thinking my training is not ok, but as you have
the same result probably this is the only difference?!


Best regards,

Detlef

Am 02.02.2016 um 03:25 schrieb Hiroshi Yamashita:
> Hi,
> 
> I made DCNN, and tried whether DCNN can understand semeai.
> 
> 1. try one playout that always select DCNN highest probablity
> move. 2. try 100 playouts that select moves from DCNN probability. 
> (one playout takes 4 seconds.)
> 
> Result is DCNN does not understand semeai. It can play semeai like
> moves, but far from perfect. Maybe need more difficult feature?
> 
> DCNN highestDCNN 100 playouts Aya playout problem1   0
> 53   20 problem2   0 54
> 85 problem3 100 91   70 
> problem4 100 66   66 problem5
> 0 51   62 problem6   0
> 45   67 problem7 100 95
> 90 problem8   0  9   90 
> problem9   0 39   50 
>  Average
> 33 56   67
> 
> 100 is correct. 0 is wrong. 53 means DCNN playout is correct 53
> times out of 100 playouts.
> 
> 
> DCNN is  12 layers, 128 filters. (5x5_128, 3x3_128 x10, 3x3_1) It
> predicts next 3 moves, like Facebook paper. Test accuracy is next_1
> 49%, next_2 27%, next_3 16%. 
> http://www.yss-aya.com/20160123_3steps.png Feature are liberty
> black and white. 1,2,3,4>= stones black and white. 1,2,3,4>= 
> previous move 1,2,3,4,5 previous ko 1,2,3 CFG Distance 1,2,3,4,5>= 
> string life and death by search. dead, killed next, kill move, life
> move. 8 planes group life and death. It is from Aya's classic
> evaluation(KGS 8k). 6 planes territory. black and white. half eye.
> black and white. recapture soon. (if black play here, recapture
> soon). black and white All 49 channels. Learning games are from
> GoGoD, 78000 games. This DCNN runs on CGOS by DCNN_Aya_i49_a49, no
> search. GTX 980. Its winrate 40% against Pachi 100k. AlphaGo
> DCNN(RL) winrate is 85% against Pachi 100k. So this DCNN is weaker
> than AlphaGo(RL) by 370 Elo, and weaker than AlphaGo(SL) by 129
> Elo.
> 
> 
> Problem8, W lives. 4 libs vs 5 libs (top left) Problem9, W lives. 5
> libs vs 6 libs (bottom left)
> 
> 19.O.X.XO.OX.   DCNN answered badly these two problem. 
> 18OXO.XX. 17.OO.X.O.O..   Left Top is 4 libs vs
> 5 libs 16OO.X...O...   White(O) must live 
> 15.XX   14X...O..   DCNN answer is
> White(O) lives 9% 13...   DCNN best move playout
> also fails. 12... 11O.. 
> 10... 9O.. 8XX.X... 
> 7.OX.X..O... 6O.X 5OX..O..
> Left bottom is 5 libs vs 6 libs. 4OX.O...  White(O)
> must live 3OX..O.. 2XO.XOX.  DCNN answer is
> White(O) lives 39% 1.OOXOX.  DCNN best move playout
> also fails. ABCDEFGHJKLMNOPQRST
> 
> (;GM[1]SZ[19]KM[0.5]RU[Chinese]AB[da][fa][ja][bb][cb][db][eb][fb][ib]
>
> 
[jb][ic][dd][ed][fd][gd][hd][be][ce][af][al][bl][dl][cm][em][cn][fo]
> [fp][aq][bq][cq][dq][fq][ar][dr][fr][ds][fs]AW[ba][ga][ia][ab][gb] 
> [bc][cc][dc][ec][fc][gc][oc][qc][ad][bd][pd][qf][qi][qk][bm][pm][an]
>
> 
[ao][qo][ap][bp][cp][dp][ep][pp][eq][qq][br][er][bs][cs][es])
> 
> 
> Problem1, B lives. 3 libs vs 5 libs. share 2 libs. (top) Problem2,
> B lives. (right bottom)
> 
> (;GM[1]SZ[19]KM[0.5]TM[]RU[Chinese];B[pe];W[qp];B[dq];W[cd];B[op]; 
> W[do];B[fq];W[cq];B[cp];W[dp];B[bq];W[cr];B[ep];W[dr];B[eq];W[co]; 
> B[bp];W[dk];B[bo];W[bn];B[cn];W[dn];B[cm];W[dm];B[cl];W[dl];B[pm]; 
> W[oo];B[po];W[pp];B[oq];W[no];B[qo];W[ro];B[rn];W[rp];B[qm];W[mq]; 
> B[qr];W[rr];B[qq];W[rq];B[nr];W[mr];B[rs];W[pq];B[pr];W[qs];B[ps]; 
> W[sr];B[ns];W[nm];B[so];W[pi];B[sp];W[ed];B[jd];W[ld];B[gd];W[lf]; 
> B[nd];W[jf];B[ec];W[dc];B[fc];W[gf];B[he];W[ig];B[fn];W[fo];B[eo]; 
> W[ck];B[bm];W[fm];B[gn];W[gm];B[hn];W[hm];B[in];W[im];B[jn];W[jq]; 
> B[jm];W[hq];B[jl];W[en];B[gp];W[pg];B[qf];W[qc];B[oc];W[oe];B[pc]; 
> W[pd];B[qd];W[od];B[mc];W[rd];B[qe];W[rc];B[rh];W[qg];B[rg];W[lc]; 
> B[qb];W[rb];B[pb];W[re];B[rf];W[qk];B[ra];W[ne];B[se];W[md];B[nc]; 
> W[jb];B[ic];W[ib];B[hb];W[hc];B[hd];W[ha];B[gb];W[ie];B[fe];W[id]; 
> B[jc];W[kb];B[ff];W[fg];B[eg];W[dg];B[df];W[eh];B[de];W[dd];B[db]; 
>

[Computer-go] Last CFP: GCIRE2016 | GREEN COMPUTING, INTELLIGENT AND RENEWABLE ENERGIES | Philippines

2016-02-01 Thread Mari Glambert

THE SECOND INTERNATIONAL CONFERENCE ON GREEN COMPUTING, INTELLIGENT AND
RENEWABLE ENERGIES (GCIRE2016)

Where: University of Perpetual Help System DALTA, Las Piñas, Manila,
Philippines
When: February 24-26, 2016
Website: http://bit.do/gcire2016

Publication: The Society of Digital Information and Wireless Communications
Digital Library, conference proceedings, other indexing sites and special
issues on (1) International Journal of New Computer Architectures and their
Applications; (2) International Journal of Digital Information and Wireless
Communications; International Journal of Cyber-Security and Digital
Forensics; (4) International Journal of E-Learning and Educational
Technologies in the Digital Media.

Keynote Speaker: Dr. Ekaterina Pshehotskaya, InfoWatch, Russia
Keynote Title: Linguistics in Data Loss Prevention

Submission Link: http://bit.do/gcire2016-papersubmission
Full Paper Submission Deadline: February 5, 2016

Accepted Paper/s Guidelines Link: http://bit.do/gcire2016-guidelines
Files Required: Final Paper and Copyright

Registration Link: http://bit.do/gcire2016-registration
First Paper Registration: $390
Second Paper (or more) Registration: $290
Guest Registration: $250

Paper Registration Deadline: February 14, 2016
Guest Registration Deadline: February 24, 2016

General Chair: Engr. Lorena C. Ilagan
Contact Details: http://bit.do/gcire2016-contactus

Co-event Conferences:
(1) The International Conference on Innovations in Intelligent Systems and
Computing Technologies (ICIISCT2016)
(2) The Second International Conference on Electrical and Electronic
Engineering, Telecommunication Engineering, and Mechatronics (EEETEM2016)

KINDLY FORWARD THIS ANNOUNCEMENT TO COLLEAGUES WHO MIGHT BE OF INTEREST.
GOOD DAY!
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Re: [Computer-go] The Game AI Forum is back

2016-02-01 Thread Jim O'Flaherty

Tysvm for the clarification. I appreciate and agree with your reasons. :)

On Mon, Feb 1, 2016 at 8:20 AM, Rémi Coulom  wrote:

> I did not create a go sub-forum in order to not compete with this mailing
> list. As long as there is not a strong agreement of all the members of the
> list to move there, I feel that splitting into two online discussion places
> would be detrimental. I won't censor topics about the game of Go on
> game-ai-forum.org, though, if you really want to post there.
>
> Rémi
>
> On 02/01/2016 02:56 PM, Jim O'Flaherty wrote:
>
>> Richard,
>>
>> I'm probably missing the obvious, I went to the forum, but was unable to
>> find a forum specifically for Go. I found Abolone, Hex and several others.
>>
>>
>> Thank you,
>>
>> Jim
>>
>>
>> On Fri, Jan 1, 2016 at 9:08 AM, Richard Lorentz > > wrote:
>>
>> Thank you very much. Personally I find it much easier to keep up
>> with and follow topics in this kind of format. Perhaps we can
>> encourage people who post on the mailing list to post on your
>> Forum instead/too?
>>
>> -Richard
>>
>> P.S. Happy New Year!
>>
>>
>> On 01/01/2016 12:56 AM, Rémi Coulom wrote:
>>
>> Hi,
>>
>> I had created the Game Programming Forum a few years ago. I
>> decided to put it online again, at a new URL:
>>
>> https://urldefense.proofpoint.com/v2/url?u=http-3A__www.game-2Dai-2Dforum.org_=CwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=k7ciuFNHc56S8sYi-wsSC3UjQqtMtd8vZwuQLSElt0U=Qe20Mv7Kkbe7tMuviF1S9NBlJEB_lWpPF2m-yVhiEJY=
>>
>> Maybe some of you will be interested to participate there.
>>
>> Rémi
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>>
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Re: [Computer-go] The Game AI Forum is back

2016-02-01 Thread Rémi Coulom

I did not create a go sub-forum in order to not compete with this 
mailing list. As long as there is not a strong agreement of all the 
members of the list to move there, I feel that splitting into two online 
discussion places would be detrimental. I won't censor topics about the 
game of Go on game-ai-forum.org, though, if you really want to post there.


Rémi

On 02/01/2016 02:56 PM, Jim O'Flaherty wrote:

Richard,

I'm probably missing the obvious, I went to the forum, but was unable 
to find a forum specifically for Go. I found Abolone, Hex and several 
others.



Thank you,

Jim


On Fri, Jan 1, 2016 at 9:08 AM, Richard Lorentz 
> wrote:


Thank you very much. Personally I find it much easier to keep up
with and follow topics in this kind of format. Perhaps we can
encourage people who post on the mailing list to post on your
Forum instead/too?

-Richard

P.S. Happy New Year!


On 01/01/2016 12:56 AM, Rémi Coulom wrote:

Hi,

I had created the Game Programming Forum a few years ago. I
decided to put it online again, at a new URL:

https://urldefense.proofpoint.com/v2/url?u=http-3A__www.game-2Dai-2Dforum.org_=CwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=k7ciuFNHc56S8sYi-wsSC3UjQqtMtd8vZwuQLSElt0U=Qe20Mv7Kkbe7tMuviF1S9NBlJEB_lWpPF2m-yVhiEJY=

Maybe some of you will be interested to participate there.

Rémi
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Robert Jasiek


On 01.02.2016 15:15, Jim O'Flaherty wrote:

I'm not seeing the ROI in attempting to map human idiosyncratic linguistic
systems to/into a Go engine. Which language would be the one to use;
English, Chinese, Japanese, etc? As abstraction goes deeper, the nuance of
each human language diverges from the others (due to the way the human
brain is just a fractal based analogy making engine). [...]

> unless you are, of course, suggesting that is something

you are taking up. :)


The human language for interaction with / translation to programming 
language includes


- well-defined terms / concepts
- rules / principles with stated presuppositions
- methods / procedures / informal algorithms
- proofs / strong evidence for the aforementioned being correct / 
successful (always or to some extent)


Of course, I am an example of a person having been doing this for many 
years. In fact, I might be the leading generalist for go theory expert 
knowledge stated in writing.



The AI world is changing to make explaining computation cognition to humans
less necessary, or even desirable.


I disagree strongly.

Almost all the AI world has done is creating strong programs. Explaining 
human thinking and explaining program thinking in terms of human 
thinking is as important as it has always been.



Why bound the solution space to only
what cognitively linguistically limited humans can imagine and/or consider?


Indeed. I prefer to exceed limitations by creating new terms, 
definitions for undefined terms, principles, methods etc. Human beings 
can better learn if they know what to learn because the contents is 
described clearly.



about what is rapidly
approaching as human cognition automateable.


Eh? Besides GoTools, there has been very little, AFAIK.

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

2016-02-01 Thread Hideki Kato

Thanks, it's very reasonable :)

Hideki

Michael Markefka: 
: 
>On Mon, Feb 1, 2016 at 1:44 PM, Hideki Kato  wrote:
>> I was, btw, really surprised when Zen beat fj with two stones
>> handi.
>> http://files.gokgs.com/games/2016/1/31/Zen19X-fj.sgf
>>
>> Hideki
>
>On the DGoB forums fj stated, possibly in jest, that this was an even
>game, as he had had a glass of wine for every stone of handicap there.
>Seems like he wasn't in the most competitive of moods. Still a good
>game though. :)
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Ingo Althöfer

Hi Hideki,

first of all congrats to the nice performance of Zen over the weekend!

> Ingo and all,
> Why you care AlphaGo and DCNN so much?  

I can speak only for myself. DCNNs may be not only applied to
achieve better playing strength. One may use them to create
playing styles, or bots for go variants.

One of my favorites is robot frisbee go. 
http://www.althofer.de/robot-play/frisbee-robot-go.jpg
Perhaps one can teach robots with DCNN to throw the disks better.

And my expectation is: During 2016 we will see many more fantastic
applications of DCNN, not only in Go. (Olivier had made a similar
remark already.)

Ingo.

PS. Dietmar Wolz, my partner in space trajectory design, just told me
that in his company they started woth deep learning...
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Re: [Computer-go] Match Conditions?

2016-02-01 Thread Ingo Althöfer

Hi Aja,

congratulations again to the fantastic achievement of your team!

One bunch of management questions:
 
* How many games will be played in March between Alpha-Go and Lee Sedol?
* Will it be just "X games" or some "best of X" format?

* What will be the thinking times?
* Will there be rest days between the rounds?

* Would it be ok for DeepMind, when Lee Sedol takes one or two coaches
from the computer-go scene?

Cheers, Ingo.

Gesendet: Montag, 01. Februar 2016 um 14:38 Uhr
Von: "Aja Huang" 
An: computer-go@computer-go.org
Betreff: Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks 
and Tree Search

Hi Petr,
 
On Mon, Feb 1, 2016 at 11:38 AM, Petr Baudis  wrote:That's right, 
but unless I've overlooked something, I didn't see Fan Hui
create any complicated fight, there wasn't any semeai or complex
life (besides the by-the-book oonadare).  This, coupled with the
fact that there is no new mechanism to deal with these (unless the value
network has truly astonishing generalization capacity, but it just
remembering common tsumego and joseki shapes is imho a simpler
explanation), leads me to believe that it remains a weakness.
 
If you check Myungwan Kim 9p's comments in the video, in the 4th game there was 
a semeai that AlphaGo read out at top side. See the game at
 
http://britgo.org/deepmind2016/summary[http://britgo.org/deepmind2016/summary]
 
Unfortunately before the Lee match I'm not allowed to answer some of the 
interesting questions raised in this thread, or mention how strong is AlphaGo 
now. But for now what I can say is that in the nature paper (about 5 months 
ago) AlphaGo reached nearly 100% win rate against the latest commercial 
versions of Crazy Stone and Zen, and AlphaGo still did well even on 4 handicap 
stones, suggesting AlphaGo may do much better in tactical situations than Crazy 
Stone and Zen. 
 
I understand you bet on Lee but I hope you will enjoy watching the match. :)
 
Aja
 Of course there are other possibilities, like AlphaGo always steering
the game in a calmer direction due to some emergent property.  But
sometimes, you just have to go for the fight, don't you?

                                Petr Baudis

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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

2016-02-01 Thread Robert Jasiek


On 01.02.2016 14:38, Aja Huang wrote:

AlphaGo may do much better in tactical
situations than Crazy Stone and Zen.


Judging very quickly from the Fan Hui games, AlphaGo's group-local 
"reading" is very deep and accurate but I'd need to read for myself 
equally deeply and carefully before I would want to confirm Myongwan 
Kim's related opinion.


--
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Re: [Computer-go] The Game AI Forum is back

2016-02-01 Thread Jim O'Flaherty

Richard,

I'm probably missing the obvious, I went to the forum, but was unable to
find a forum specifically for Go. I found Abolone, Hex and several others.


Thank you,

Jim


On Fri, Jan 1, 2016 at 9:08 AM, Richard Lorentz 
wrote:

> Thank you very much. Personally I find it much easier to keep up with and
> follow topics in this kind of format. Perhaps we can encourage people who
> post on the mailing list to post on your Forum instead/too?
>
> -Richard
>
> P.S. Happy New Year!
>
>
> On 01/01/2016 12:56 AM, Rémi Coulom wrote:
>
>> Hi,
>>
>> I had created the Game Programming Forum a few years ago. I decided to
>> put it online again, at a new URL:
>> https://urldefense.proofpoint.com/v2/url?u=http-3A__www.game-2Dai-2Dforum.org_=CwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=k7ciuFNHc56S8sYi-wsSC3UjQqtMtd8vZwuQLSElt0U=Qe20Mv7Kkbe7tMuviF1S9NBlJEB_lWpPF2m-yVhiEJY=
>> Maybe some of you will be interested to participate there.
>>
>> Rémi
>> ___
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>>
>
>
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