Re: [Computer-go] Congratulations to AlphaGo (Statistical significance of results)

[Thomas Wolf]

I am sorry, but I think this discussion is a bit pointless.
While I write these 3 lines and you read them, AlphGo got 20 ELO 
points stronger. :-)


Thomas

On Tue, 22 Mar 2016, Lucas, Simon M wrote:



Still an interesting question is how one could make

more powerful inferences by observing the skill of

the players in each action they take rather than just

the final outcome of each game.

 

If you saw me play a single game of tennis against Federer

you’d have no doubt as to which way the next 100 games would go.

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of 
Álvaro Begué
Sent: 22 March 2016 17:21
To: computer-go 
Subject: Re: [Computer-go] Congratulations to AlphaGo (Statistical significance 
of results)

 

A very simple-minded analysis is that, if the null hypothesis is that AlphaGo 
and Lee Sedol are
equally strong, AlphaGo would do as well as we observed or better 15.625% of 
the time. That's a
p-value that even social scientists don't get excited about. :)

Álvaro.

 

On Tue, Mar 22, 2016 at 12:48 PM, Jason House  
wrote:

  Statistical significance requires a null hypothesis... I think it's 
probably easiest to
  ask the question of if I assume an ELO difference of x, how likely it's a 
4-1 result?
  Turns out that 220 to 270 ELO has a 41% chance of that result.
  >= 10% is -50 to 670 ELO
  >= 1% is -250 to 1190 ELO
  My numbers may be slightly off from eyeballing things in a simple excel 
sheet. The idea
  and ranges should be clear though

  On Mar 22, 2016 12:00 PM, "Lucas, Simon M"  wrote:

Hi all,

I was discussing the results with a colleague outside
of the Game AI area the other day when he raised
the question (which applies to nearly all sporting events,
given the small sample size involved)
of statistical significance - suggesting that on another week
the result might have been 4-1 to Lee Sedol.

I pointed out that in games of skill there's much more to judge 
than just the
final
outcome of each game, but wondered if anyone had any better (or 
worse :)
arguments - or had even engaged in the same type of
conversation.

With AlphaGo winning 4 games to 1, from a simplistic
stats point of view (with the prior assumption of a fair
coin toss) you'd not be able to claim much statistical
significance, yet most (me included) believe that
AlphaGo is a genuinely better Go player than Lee Sedol.

From a stats viewpoint you can use this approach:
http://www.inference.phy.cam.ac.uk/itprnn/book.pdf
(see section 3.2 on page 51)

but given even priors it won't tell you much.

Anyone know any good references for refuting this
type of argument - the fact is of course that a game of Go
is nothing like a coin toss.  Games of skill tend to base their
outcomes on the result of many (in the case of Go many hundreds of)
individual actions.

Best wishes,

  Simon


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Re: [Computer-go] Congratulations to AlphaGo

[Thomas Wolf]

On Sat, 12 Mar 2016, Lukas van de Wiel wrote:


And the hardware available for this tournament was tremendous. It remains to be 
seen whether the hardware and the people
maintaining it would be available for a longer period. The costs of this are 
not to be underestimated. Who would pay it?


The AlphaGo team would get feedback from testing by players with very
different ideas/strengths who they would otherwise never get in contact with.

For example, Michael Redmond mentioned repeatedly in the last 3 reviews that
he would love to play AlphaGo to study Go, to find new openings,...



Lukas

On Sun, Mar 13, 2016 at 12:20 PM, Clark B. Wierda <cbwie...@gmail.com> wrote:
  On Sat, Mar 12, 2016 at 5:05 PM, Thomas Wolf <tw...@brocku.ca> wrote:
Having AlphaGo playing exclusively on KGS would be such a boost to 
KGS!

  For sure.

The other Go servers might have their own opinion on that.

Clark 

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Re: [Computer-go] Congratulations to AlphaGo

[Thomas Wolf]

Having AlphaGo playing exclusively on KGS would be such a boost to KGS!

On Sat, 12 Mar 2016, Brian Sheppard wrote:



Play on KGS. Pros can be anonymous, and test themselves and AlphaGo at the same 
time. :-)

 

From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Jim 
O'Flaherty
Sent: Saturday, March 12, 2016 4:56 PM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] Congratulations to AlphaGo

 

I think you're correct, Thomas. The challenge is going to be getting ANY 
professional to be the one who
"takes handicap stones" for the first time in years. The possible "shame" of 
doing so is what will make
it messy.

 

Once someone does take that step, though, I think it is only a matter of time 
before the rating of
humans will be made a subordinate rating relative to the "objective" rating of 
the AIs, AlphaGo just
being the first. And that has its own psychological challenges as the Go world 
has many decades of
handling ELOs and rankings for humans. So, I don't think change in this area is 
going to be welcomed
anytime soon.

 

 

On Sat, Mar 12, 2016 at 3:03 PM, Thomas Wolf <tw...@brocku.ca> wrote:

  Chris,

  Prompted from a discussion on the computer go email list
  (and my last email today) :

  We currently have no measure at all to judge how safe a winor loss is at 
any
  stage of the game. The measure applied currently of counting territory 
does
  only apply if both players try to maximize territory but not if at least 
one
  player maximizes the chance of winning. (I know, it was mentioned 
already).

  But really, comments like "Player ... is catching up" are pretty 
meaningless
  and are only valid if one explicitly mentions points or territorry, and 
adds
  that this has nothing to do with winning probabilities.

  Even the winning percentages provided by the computer programs themselves 
are
  no real indicator for winninig chances. They are tools to find the best 
move
  and are a statistical measure over several playout sequences based on 
selfplay
  not based on play against that opponent. Equally, winning percentages 
worked
  out by other computer programs are also not adequate (although they are at
  least unbiased) because they do also not use the real opponents to play 
out
  the sequences.

  The only valid strength indicator would be to gradually increase handicap
  stones or komi for the previous loser in a series of games.

  Regards,
  Thomas



  On Sat, 12 Mar 2016, Sorin Gherman wrote:


It is fascinating indeed to try to find how much stronger is AlphaGo
compared to top humans.

Given the fact that it is hard to find the reason why Lee Sedol 
lost, and
that AlphaGo seems to get mysteriously ahead without a clear 
reason, tells
me that the difference is definitely more than one stone handicap, 
maybe 2+
stones, as crazy as it may sound given Lee Sedol's level.

I am pretty sure he will not accept to play with handicap against 
AlphaGo
though. Maybe "younger wolves" like Ke Jie will though and we will 
find out.

On Mar 12, 2016 11:03 AM, "Thomas Wolf" <tw...@brocku.ca> wrote:
      A suggestion for possible future games to be arranged between
      AlphaGo and
      strong players:

      Whoever lost shall be given 1 stone or the equivalent of 1/2
      stone handcap in the
      next game. Games should continue until each side has won at
      least once. This
      way AlphaGo will be forced to demonstrate its full strength 
over
      a whole game
      which we are all too curious to see.

      Thomas

      On Sat, 12 Mar 2016, Aja Huang wrote:

            Thanks all. AlphaGo has won the match against Lee
            Sedol. But there are still 2 games to play.
            Aja

            On Sat, Mar 12, 2016 at 5:49 PM, Jim O'Flaherty
            <jim.oflaherty...@gmail.com> wrote:
                  It was exhilerating to witness history being
            made! Awesome!

            On Sat, Mar 12, 2016 at 2:17 AM, David Fotland
            <fotl...@smart-games.com> wrote:

                  Tremendous games by AlphaGo.  Congratulations!

                   

                  From: Computer-go
            [mailto:computer-go-boun...@computer-go.org] On
            Behalf Of Lukas van de Wiel
                  Sent: Saturday, March 12, 2016 12:14 AM
                  To: computer-go@computer-go.org
       

Re: [Computer-go] Congratulations to AlphaGo

[Thomas Wolf]

Chris,

Prompted from a discussion on the computer go email list
(and my last email today) :

We currently have no measure at all to judge how safe a winor loss is at any
stage of the game. The measure applied currently of counting territory does
only apply if both players try to maximize territory but not if at least one
player maximizes the chance of winning. (I know, it was mentioned already).

But really, comments like "Player ... is catching up" are pretty meaningless
and are only valid if one explicitly mentions points or territorry, and adds
that this has nothing to do with winning probabilities.

Even the winning percentages provided by the computer programs themselves are
no real indicator for winninig chances. They are tools to find the best move
and are a statistical measure over several playout sequences based on selfplay
not based on play against that opponent. Equally, winning percentages worked
out by other computer programs are also not adequate (although they are at
least unbiased) because they do also not use the real opponents to play out
the sequences.

The only valid strength indicator would be to gradually increase handicap
stones or komi for the previous loser in a series of games.

Regards,
Thomas

On Sat, 12 Mar 2016, Sorin Gherman wrote:



It is fascinating indeed to try to find how much stronger is AlphaGo
compared to top humans.

Given the fact that it is hard to find the reason why Lee Sedol lost, and
that AlphaGo seems to get mysteriously ahead without a clear reason, tells
me that the difference is definitely more than one stone handicap, maybe 2+
stones, as crazy as it may sound given Lee Sedol's level.

I am pretty sure he will not accept to play with handicap against AlphaGo
though. Maybe "younger wolves" like Ke Jie will though and we will find out.

On Mar 12, 2016 11:03 AM, "Thomas Wolf" <tw...@brocku.ca> wrote:
  A suggestion for possible future games to be arranged between
  AlphaGo and
  strong players:

  Whoever lost shall be given 1 stone or the equivalent of 1/2
  stone handcap in the
  next game. Games should continue until each side has won at
  least once. This
  way AlphaGo will be forced to demonstrate its full strength over
  a whole game
  which we are all too curious to see.

  Thomas

  On Sat, 12 Mar 2016, Aja Huang wrote:

Thanks all. AlphaGo has won the match against Lee
Sedol. But there are still 2 games to play.
Aja

On Sat, Mar 12, 2016 at 5:49 PM, Jim O'Flaherty
<jim.oflaherty...@gmail.com> wrote:
      It was exhilerating to witness history being
made! Awesome!

On Sat, Mar 12, 2016 at 2:17 AM, David Fotland
<fotl...@smart-games.com> wrote:

      Tremendous games by AlphaGo.  Congratulations!

       

      From: Computer-go
[mailto:computer-go-boun...@computer-go.org] On
Behalf Of Lukas van de Wiel
      Sent: Saturday, March 12, 2016 12:14 AM
      To: computer-go@computer-go.org
      Subject: [Computer-go] Congratulations to
AlphaGo

 

Whoa, what a fight! Well fought, and well won!

Lukas


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Re: [Computer-go] Congratulations to AlphaGo

[Thomas Wolf]

Hi Ingo,

I have the manuscript of 2 books with each 100 computer generated problems
which 1st class insei Yutae Seo (Korea) picked out of 20,000 computer
generated problems, working for 5 months on this selection. Many have a tricky
ko status. I am happy to provide them.

Simpler even and more revealing would be to write down semeai problems using,
for example, the work of Teigo Nakamura. These problems can be evaluated in no
time once one understood the math but which take arbitrarily long to solve if
a brute force search would be applied. Simple pattern matching should not help
there.

Finally, there are seki problems which I showed several professional players,
including famous 9p who could not tell whether the game was over or not.

Lot's of fun tests one could do.

Cheers, Thomas.

On Sat, 12 Mar 2016, "Ingo Althöfer" wrote:


Hi Thomas,

Von: "Thomas Wolf" <tw...@brocku.ca>

A suggestion for possible future games to be arranged between AlphaGo and
strong players:

Whoever lost shall be given 1 stone or the equivalent of 1/2 stone handcap in 
the
next game. Games should continue until each side has won at least once. This
way AlphaGo will be forced to demonstrate its full strength over a whole game
which we are all too curious to see.


That is one interesting proposal. I have another one:
You are the master of computer tsume go.
Give DeepMind a set of your tsume go compositions (from easy
to really difficult) and let them test which of the problems
AlphaGo can solve.

Cheers, Ingo.
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Re: [Computer-go] Congratulations to AlphaGo

[Thomas Wolf]

A suggestion for possible future games to be arranged between AlphaGo and
strong players:

Whoever lost shall be given 1 stone or the equivalent of 1/2 stone handcap in 
the
next game. Games should continue until each side has won at least once. This
way AlphaGo will be forced to demonstrate its full strength over a whole game
which we are all too curious to see.

Thomas

On Sat, 12 Mar 2016, Aja Huang wrote:


Thanks all. AlphaGo has won the match against Lee Sedol. But there are still 2 
games to play.
Aja

On Sat, Mar 12, 2016 at 5:49 PM, Jim O'Flaherty  
wrote:
  It was exhilerating to witness history being made! Awesome!

On Sat, Mar 12, 2016 at 2:17 AM, David Fotland  wrote:

  Tremendous games by AlphaGo.  Congratulations!

   

  From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf 
Of Lukas van de Wiel
  Sent: Saturday, March 12, 2016 12:14 AM
  To: computer-go@computer-go.org
  Subject: [Computer-go] Congratulations to AlphaGo

 

Whoa, what a fight! Well fought, and well won!

Lukas


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Re: [Computer-go] Finding Alphago's Weaknesses

[Thomas Wolf]

With at most 2x361 or so different end scores but 10^{XXX} possible different
games, there are at least in the opening many moves with the same optimal
outcome. The difference between these moves is not the guaranteed score (they
are all optimal) but the difficulty to play optimal after that move. And the
human and computer strengths are rather different.

On Thu, 10 Mar 2016, uurtamo . wrote:



If that's the case, then they should be able to give opinions on best first 
moves, best first two move combos, and best first three move combos. That'd
be interesting to see. (Top 10 or so of each).

s.

On Mar 10, 2016 12:37 PM, "Sorin Gherman" <sor...@gmail.com> wrote:

  From reading their article, AlphaGo makes no difference at all between 
start, middle and endgame.
  Just like any other position, the empty (or almost empty, or almost full) 
board is just another game position in which it chooses (one of)
  the most promising moves in order to maximize her chance of winning.

  On Mar 10, 2016 12:31 PM, "uurtamo ." <uurt...@gmail.com> wrote:

Quick question - how, mechanically, is the opening being handled by 
alpha go and other recent very strong programs? Giant
hand-entered or game-learned joseki books?

Thanks,

steve

    On Mar 10, 2016 12:23 PM, "Thomas Wolf" <tw...@brocku.ca> wrote:
  My 2 cent:

  Recent strong computer programs never loose by a few points.  
They are either
  crashed before the end game starts (because when being 
clearly behind they play more
  desperate and weaker moves because they mainly get negative 
feadback from
  their search with mostly loosing branches and risky play 
gives them the only
  winning sequences in their search) or they win by resignation 
or win
  by a few points.

  In other words, if a human player playing AlphaGo does not 
have a large
  advantage already in the middle game, then AlphaGo will win 
whether it looks
  like it or not (even to a 9p player like Michael Redmond was 
surprised
  last night about the sudden gain of a number of points by 
AlphaGo in the
  center in the end game: 4:42:10, 4:43:00, 4:43:28 in the 
video https://gogameguru.com/alphago-2/)

  In the middle and end game the reduced number of possible 
moves and the
  precise and fast counting ability of computer programs are 
superior.  In the
  game commentary of the 1st game it was mentioned that Lee 
Sedol considers the
  opening not to be his strongest part of the game.  But with 
AlphaGo playing
  top pro level even in the opening, a large advantage after 
the middle game
  might simply be impossible to reach for a human.

  About finding weakness:
  In the absense of games of AlphaGo to study it might be 
interesting to get a general idea by checking out the games
  where 7d Zen lost on KGS
  recently.

  Thomas

  On Thu, 10 Mar 2016, wing wrote:

One question is whether Lee Sedol knows about these 
weaknesses.
Another question is whether he will exploit those 
weaknesses.
Lee has a very simple style of play that seems less 
ko-oriented
than other players, and this may play into the hands of 
Alpha.

Michael Wing

   I was surprised the Lee Sedol didn't take the 
game a bit further to
   probe AlphaGo and see how it responded to 
[...complex kos, complex ko
   fights, complex sekis, complex semeais, ..., 
multiple connection
   problems, complex life and death problems] as 
ammunition for his next
   game. I think he was so astonished at being put 
into a losing
   position, he wasn't mentally prepared to put 
himself in a student's
   role again, especially to an AI...which had 
clearly played much weaker
   games just 6 months ago. I'm hopeful Lee Sedol's 
team has been some
   meta-strategy sessions where, if he finds 
himself in a losing position
   in game two, he turns it into exploring a set of 
experiments to tease
   out some of the weaknesses to be better 
exploited in the remaining
   games.

   On Thu, Mar 10, 2016 at 8:16 AM, Robert Jasiek 
<jas...@snafu.de> wrote:

 

Re: [Computer-go] Finding Alphago's Weaknesses

[Thomas Wolf]

But at the start of the game the statistical learning of infinitessimal
advantages of one opening move compared to another opening move is less
efficient than the learning done in the middle and end game.

On Thu, 10 Mar 2016, Sorin Gherman wrote:



From reading their article, AlphaGo makes no difference at all between start, 
middle and endgame.
Just like any other position, the empty (or almost empty, or almost full) board 
is just another game position in which it chooses (one of) the most
promising moves in order to maximize her chance of winning.

On Mar 10, 2016 12:31 PM, "uurtamo ." <uurt...@gmail.com> wrote:

  Quick question - how, mechanically, is the opening being handled by alpha 
go and other recent very strong programs? Giant hand-entered or
  game-learned joseki books?

  Thanks,

  steve

  On Mar 10, 2016 12:23 PM, "Thomas Wolf" <tw...@brocku.ca> wrote:
My 2 cent:

Recent strong computer programs never loose by a few points.  They 
are either
crashed before the end game starts (because when being clearly 
behind they play more
desperate and weaker moves because they mainly get negative 
feadback from
their search with mostly loosing branches and risky play gives them 
the only
winning sequences in their search) or they win by resignation or win
by a few points.

In other words, if a human player playing AlphaGo does not have a 
large
advantage already in the middle game, then AlphaGo will win whether 
it looks
like it or not (even to a 9p player like Michael Redmond was 
surprised
last night about the sudden gain of a number of points by AlphaGo 
in the
center in the end game: 4:42:10, 4:43:00, 4:43:28 in the video 
https://gogameguru.com/alphago-2/)

In the middle and end game the reduced number of possible moves and 
the
precise and fast counting ability of computer programs are 
superior.  In the
game commentary of the 1st game it was mentioned that Lee Sedol 
considers the
opening not to be his strongest part of the game.  But with AlphaGo 
playing
top pro level even in the opening, a large advantage after the 
middle game
might simply be impossible to reach for a human.

About finding weakness:
In the absense of games of AlphaGo to study it might be interesting 
to get a general idea by checking out the games where 7d Zen
lost on KGS
recently.

Thomas

On Thu, 10 Mar 2016, wing wrote:

  One question is whether Lee Sedol knows about these 
weaknesses.
  Another question is whether he will exploit those weaknesses.
  Lee has a very simple style of play that seems less 
ko-oriented
  than other players, and this may play into the hands of Alpha.

  Michael Wing

 I was surprised the Lee Sedol didn't take the game a 
bit further to
 probe AlphaGo and see how it responded to [...complex 
kos, complex ko
 fights, complex sekis, complex semeais, ..., multiple 
connection
 problems, complex life and death problems] as 
ammunition for his next
 game. I think he was so astonished at being put into a 
losing
 position, he wasn't mentally prepared to put himself 
in a student's
 role again, especially to an AI...which had clearly 
played much weaker
 games just 6 months ago. I'm hopeful Lee Sedol's team 
has been some
 meta-strategy sessions where, if he finds himself in a 
losing position
 in game two, he turns it into exploring a set of 
experiments to tease
 out some of the weaknesses to be better exploited in 
the remaining
 games.

 On Thu, Mar 10, 2016 at 8:16 AM, Robert Jasiek 
<jas...@snafu.de> wrote:

>  On 10.03.2016 00:45, Hideki Kato wrote:
> > >  such as solving complex semeai's and 
double-ko's, aren't solved yet.
> >  To find out Alphago's weaknesses, there can be, in 
particular,
> >  - this match
>  - careful analysis of its games
>  - Alphago playing on artificial problem positions incl. 
complex kos, >  complex ko fights, complex
sekis, complex semeais, complex endgames, >  multiple 
connection problems, complex life and death
problems (such as >  Igo Hatsu Yoron 120) etc., and 
then theoretical analysis of suc

Re: [Computer-go] Finding Alphago's Weaknesses

[Thomas Wolf]

My 2 cent:

Recent strong computer programs never loose by a few points.  They are either
crashed before the end game starts (because when being clearly behind they play 
more
desperate and weaker moves because they mainly get negative feadback from
their search with mostly loosing branches and risky play gives them the only
winning sequences in their search) or they win by resignation or win
by a few points.

In other words, if a human player playing AlphaGo does not have a large
advantage already in the middle game, then AlphaGo will win whether it looks
like it or not (even to a 9p player like Michael Redmond was surprised
last night about the sudden gain of a number of points by AlphaGo in the
center in the end game: 4:42:10, 4:43:00, 4:43:28 in the video 
https://gogameguru.com/alphago-2/)


In the middle and end game the reduced number of possible moves and the
precise and fast counting ability of computer programs are superior.  In the
game commentary of the 1st game it was mentioned that Lee Sedol considers the
opening not to be his strongest part of the game.  But with AlphaGo playing
top pro level even in the opening, a large advantage after the middle game
might simply be impossible to reach for a human.

About finding weakness:
In the absense of games of AlphaGo to study it might be interesting 
to get a general idea by checking out the games where 7d Zen lost on KGS

recently.

Thomas

On Thu, 10 Mar 2016, wing wrote:


One question is whether Lee Sedol knows about these weaknesses.
Another question is whether he will exploit those weaknesses.
Lee has a very simple style of play that seems less ko-oriented
than other players, and this may play into the hands of Alpha.

Michael Wing


 I was surprised the Lee Sedol didn't take the game a bit further to
 probe AlphaGo and see how it responded to [...complex kos, complex ko
 fights, complex sekis, complex semeais, ..., multiple connection
 problems, complex life and death problems] as ammunition for his next
 game. I think he was so astonished at being put into a losing
 position, he wasn't mentally prepared to put himself in a student's
 role again, especially to an AI...which had clearly played much weaker
 games just 6 months ago. I'm hopeful Lee Sedol's team has been some
 meta-strategy sessions where, if he finds himself in a losing position
 in game two, he turns it into exploring a set of experiments to tease
 out some of the weaknesses to be better exploited in the remaining
 games.

 On Thu, Mar 10, 2016 at 8:16 AM, Robert Jasiek  wrote:

>  On 10.03.2016 00:45, Hideki Kato wrote:
> 
> >  such as solving complex semeai's and double-ko's, aren't solved yet.
> 
>  To find out Alphago's weaknesses, there can be, in particular,
> 
>  - this match

>  - careful analysis of its games
>  - Alphago playing on artificial problem positions incl. complex kos, 
>  complex ko fights, complex sekis, complex semeais, complex endgames, 
>  multiple connection problems, complex life and death problems (such as 
>  Igo Hatsu Yoron 120) etc., and then theoretical analysis of such play

>  - semantic verification of the program code and interface
>  - theoretical study of the used theory and the generated dynamic data 
>  (structures)
> 
>  --

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

[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] Game Over

[Thomas Wolf]

Congratulations to Aja $ DeepMind to that great result!

I am curious to see AlphaGo having to play a tough narrow endgame.  In the
first of the 5 games it could affort not to play totally optimal in the end
and in the next 4 games Fan resigned. End games require again other, more math like 
skills, at least as human player. But maybe trained networks got good at that too.


Thomas

On Wed, 27 Jan 2016, Yuandong Tian wrote:


Congratulations to Aja & DeepMind team! Amazing results :)

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] Number of Go positions computed at last

[Thomas Wolf]

On Fri, 22 Jan 2016, Adrian Petrescu wrote:


Very cool! I find it interesting that the number is only about 1.2% of 3^361 
(though I realize 3^361 doesn't take symmetries into account).
On the surface it's counterintuitive to me that nearly 99% of random stone 
configurations are not legal Go positions!


The chance to violate the rule somewhere goes linearly with the area, so
quadratically with the size of the board.



On Fri, Jan 22, 2016 at 10:50 AM, Xavier Combelle  
wrote:
  well done !

2016-01-22 5:18 GMT+01:00 John Tromp :
  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] Seki frequencies

[Thomas Wolf]

Hi,

On Sun, 17 Jan 2016, "Ingo Althöfer" wrote:


Hi Robert,

thanks for the whole bunch of very intersting information.

Seki has AT LEAST two groups 




Sekis can have various different shapes ...
... stable anti-sekis (stable because other anti-sekis exist elsewhere on the board). 


Can you give an example for anti-seki?


Listing the possible configurations is a demanding open research field.


Perhaps you and someone like Thomas Wolf (with his life-and-dath background) would 
be "the right" people for this question.





I have an (unpublished) talk about sekis online:
http://lie.math.brocku.ca/twolf/papers/sekitalk2.pdf

I am grateful for any references about literatur on seki and any examples of
strange, exotic seki.

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Re: [Computer-go] CFP: IJCAI Computer Games Workshop 2016

[Thomas Wolf]

Hi Mark,

Thank you for the information. Unfortunately, I will not be able to attend
even though it is relatively close to my place. Would it be possible to submit
a paper and if accepted, a prerecorded talk?

Regards,
Thomas

On Sat, 16 Jan 2016, Mark Winands wrote:



Computer Games Workshop at IJCAI 2016, July 2016

---

 

Description

---

 

A workshop on computer games is to be held at IJCAI 2016 in New York City, USA. 
It is planned to publish the proceedings with Springer
in their Communications in Computer and Information Science series CCIS. The 
topics of the workshop concern all aspects of artificial
intelligence for computer games. This includes:

• Monte-Carlo methods

• Heuristic search

• Board games

• Card games

• Video games

• Perfect and imperfect information games

• Puzzles and single player games

• Multi-player games

• Serious games

• Combinatorial game theory

 

Important Dates

--

Paper Submission Deadline:  April 18th 2016

Acceptance Notification:     May, 18th 2016

Final Papers:     June, 8th 2016

 

Paper Submission Requirements

--

Papers of 10 to 15 pages in LNCS format are preferred. The file format for 
submission is PDF. Submitted papers should be sent to
tristan.cazen...@dauphine.fr

 

Tentative Program Committee

---

Christopher Archibald, Mississippi State University

Yngvi Björnsson, Reykjavik University

Bruno Bouzy, University Paris Descartes

Tristan Cazenave, University Paris Dauphine (Co-chair)

Stefan Edelkamp, University of Bremen (Co-chair)

Ryan Hayward, University of Alberta

Hiroyuki Iida, JAIST

Nicolas Jouandeau, University Paris 8

Richard Lorentz, California State University

Simon Lucas, University of Essex

Jean Méhat, University Paris 8

Martin Müller, University of Alberta

Thomas Runarsson, University of Iceland

Abdallah Saffidine, University of New South Wales

Nathan Sturtevant, University of Denver

Olivier Teytaud, University Paris Sud

Julian Togelius, New York University

Mark Winands, Maastricht University (Co-chair)

Shi-Jim Yen, National Dong Hwa University

 

 

For more information: 
http://www.lamsade.dauphine.fr/~cazenave/cgw2016/cgw2016.html


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Re: [Computer-go] 7x7 Go is weakly solved

[Thomas Wolf]

On Sun, 29 Nov 2015, 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.


Who can enforce a win with this komi?

Thomas


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] Move Evaluation in Go Using Deep Convolutional Neural Networks

[Thomas Wolf]

Last move info is a cheap hint for an instable area (unless it is a defense
move).

Thomas

On Mon, 22 Dec 2014, Stefan Kaitschick wrote:


Last move info is a strange beast, isn't it? I mean, except for ko captures, it 
doesn't really add information to the position. The
correct prediction rate is such an obvious metric, but maybe prediction 
shouldn't be improved at any price. To a certain degree, last
move info is a kind of self-delusion. A predictor that does well without it 
should be a lot more robust, even if the percentages are
poorer.

Stefan




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RE: [computer-go] benchmark tests for static evaluation functions

[Thomas Wolf]

Thanks for the comment.

On Sun, 17 Jan 2010, David Fotland wrote:

 I think you can only evaluate static evaluation in the context of a search
 and a tournament between programs.  You could start with a simple 1-ply
 search and play against gnugo.  Strength in life and death or predicting pro
 moves doesn't correlate with the ability to win games.

I know of the limited correlation, also it depends how you test the
evaluation function. Having only limited time to work on and off on Go
I do not have a game-playing program and tested the function on its own
on professional games. Anyway, my question was whether people had published
any related tests.

Thomas

 
 David
 
 -Original Message-
 From: computer-go-boun...@computer-go.org
 [mailto:computer-go-boun...@computer-go.org] On Behalf Of Thomas Wolf
 Sent: Sunday, January 17, 2010 9:03 AM
 To: computer-go@computer-go.org
 Subject: [computer-go] benchmark tests for static evaluation functions
 
 Last year I was working on a static evaluation function.
 
 Does anyone know references about published benchmark tests for static
 evaluation functions, for example, in predicting moves in professional games
 or best moves in life and death problems or predicting the status of
 semeai problems?
 
 The published benchmarks need not be for a static evaluation function in the
 traditional sense, they could be for an opening book or a MCTS program with
 very short times available.
 
 Thanks,
 
 Thomas Wolf
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[computer-go] benchmark tests for static evaluation functions

[Thomas Wolf]

Last year I was working on a static evaluation function.

Does anyone know references about published benchmark tests for static
evaluation functions, for example, in predicting moves in professional games
or best moves in life and death problems or predicting the status of
semeai problems?

The published benchmarks need not be for a static evaluation function in the
traditional sense, they could be for an opening book or a MCTS program with
very short times available.

Thanks,

Thomas Wolf
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[computer-go] end game analysis

[Thomas Wolf]

A quick question:

What programs are useful for coaching a player by analysing the moves that
have been played in the endgame of some 19x19 game?

What one would want to do is to input the position, say 30 moves from the end,
and get a ranking of the remaining moves. It would be nice if it would not be
too cumbersome to explore optimal follow up moves for any one of the moves,
i.e. to select a move and see what the winning statistics for the followup
moves is. It also should be possible to add more and more time to the analysis
to see how stable it is if more time is available. The program should be able
to use large computing resources (e.g. computing nodes with 32 CPU sharing
128GB RAM would be available).

Thanks,
Thomas
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Re: [computer-go] end game analysis

[Thomas Wolf]

A comment to my own question:

I should have formulated it better, of course all MC programs are useful in
some sense. The specifics of the request is that the player is not avalable to
play live against a normal program and to learn from interactive play. Also,
for the analysis to be more accurate and/or to investigate positions that are
earlier in the game the computing times may be too long for interactive
sessions. Ideal would be a program submitted in batch-mode which is given an
sgf file from a game and the program would analyse all positions starting with
the last move going backwards and making comments into a file.

I realize that MC programs are stronger in close games, so for each analysis
the number of prisoners might be adapted to get the best out of MC so that
from the analysis one can see where the player lost one or two points.

Thomas

On Mon, 5 Oct 2009, Thomas Wolf wrote:

 A quick question:
 
 What programs are useful for coaching a player by analysing the moves that
 have been played in the endgame of some 19x19 game?
 
 What one would want to do is to input the position, say 30 moves from the end,
 and get a ranking of the remaining moves. It would be nice if it would not be
 too cumbersome to explore optimal follow up moves for any one of the moves,
 i.e. to select a move and see what the winning statistics for the followup
 moves is. It also should be possible to add more and more time to the analysis
 to see how stable it is if more time is available. The program should be able
 to use large computing resources (e.g. computing nodes with 32 CPU sharing
 128GB RAM would be available).
 
 Thanks,
 Thomas
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Re: [computer-go] Congratulations to Fuego, the new champion!

[Thomas Wolf]

On Wed, 13 May 2009, Isaac Deutsch wrote:

 Wow, you're fast to congratulate. ;)
 
 Congratulations from me, too.

From me 3.  :)

Thomas

 
 Isaac
 -- 
 Neu: GMX FreeDSL Komplettanschluss mit DSL 6.000 Flatrate + Telefonanschluss 
 für nur 17,95 Euro/mtl.!* 
 http://dslspecial.gmx.de/freedsl-surfflat/?ac=OM.AD.PD003K11308T4569a
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[computer-go] 19x19 CGOS

[Thomas Wolf]

Is the 19x19 server down? (I wanted to look at some games.)

Thomas
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Re: [computer-go] Human Learning against MoGo

[Thomas Wolf]

On Sun, 15 Feb 2009, Ingo Althöfer wrote:

 Hello,
 ...

 When you follow this line of thought, the results of Tainan
 show that the computer go community will also now (and likely in
 future, too) have to fight with the problem/phaenomenon of quick 
 human learning (as has been the case already for several decades).

I think this is not about quick human learning, it is about missing
abilities of monte carlo programs and it takes just one game to
make them obvious to a strong player.

Thomas

 
 Ingo.
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Re: [computer-go] Re: FW: computer-go] Monte carlo play?

[Thomas Wolf]

On Sun, 16 Nov 2008, Claus Reinke wrote:

 ...
 better feeling for the game; personally, I don't like fast games(*), but
 ...

But there is this saying:

Play quick, lose quick, learn quick!   :)

Thomas
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Re: [computer-go] The Enemy's Key Point Is My Own

[Thomas Wolf]

The typical situation is that two weak chains of opposite colours
attached to each other have their few liberties (in the extreme case
their single liberty) far apart. In simple Manhatten distance you can
have these liberties easily as distant as you want, but if you think
of empty points and chains as the elementary building blocks on the
board then these liberties are still only two steps apart, separated
just by 2 chains.

Thomas

On Thu, 30 Oct 2008, Richard Brown wrote:

 Thanks to all who replied.
 
 I particularly liked David's and Gunnar's clear examples of why the
 enemy's exact key point is not always exactly my own.
 
 My foe's monkey jump is almost always better-prevented by my simple
 descent; the right distance for an extension differs for me and my
 foe; and so on.
 
 Gunnar's example contains an interesting symmetry:
 
 If it's Black (X) to play,  the 1-1 point is the worst for Black, but
 best for White.
 
 If it's White to play, the 2-2 point is worst for White, but best for Black.
 
 So from _both_ players' points-of-view, it serves as an excellent
 counter-example to that old saw, the enemy's key point is my own.
 
 On Tue, Oct 28, 2008 at 3:08 PM, Gunnar Farnebäck [EMAIL PROTECTED] wrote:
 
  An extreme case is this life and death problem where playing the
  opponent's key point is the worst you can do locally.
 
  |O.
  |O.
  |.O.XOO
  |.XOX.O
  +--
 
 -- 
 The region in which the enemy's key point lies may also contain a key
 point for me, nearby, which neutralizes the effect of the enemy's key
 point, if I play my key point first.
 
 Nah, just doesn't have the same ring to it.
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Re: [computer-go] Re: komi argument = silly

[Thomas Wolf]

On Fri, 7 Mar 2008, Petr Baudis wrote:

 On Thu, Mar 06, 2008 at 04:33:16PM -0800, Dave Dyer wrote:
  
  To a first order approximation, would changing the komi change the
  rankings?  Presumably, programs are playing the same number of games
  as black and white, so any unfair advantage or disadvantage black 
  has would balance out.
  
  Komi only matters when there is only one game between a pair of opponents.
 
 This has nothing to do with black/white distinction. The idea is to
 dynamically adjust the komi to make UCT to aim at higher and potentially
 less sure win or lower and potentially more sure loss. Of course,
 depending on whether it takes black or white you would adjust the komi
 in the correct direction.

I assume that when you change komi dynamically, all that was learned
by MC so far under the different komi value is useless/wrong.

Thomas

 
 -- 
   Petr Pasky Baudis
 Whatever you can do, or dream you can, begin it.
 Boldness has genius, power, and magic in it.  -- J. W. von Goethe
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Re: [computer-go] Bent four in the corner was:Scalability problem of play-out policies

[Thomas Wolf]

On Wed, 23 Jan 2008, Harald Korneliussen wrote:

 It turns out it's not the bent four shape, but I suspect it's
 another such shape, where more playouts only confirm that these moves
 aren't worth including into the tree, so that UCT catches them very
 late, if ever.

Just a quick note that an algorithm how to evaluate bent four like
positions with a minimax search is given in

http://lie.math.brocku.ca/twolf/papers/bent4.pdf

Thomas
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Re: [computer-go] BOINC

[Thomas Wolf]

On Tue, 30 Oct 2007, Stuart A. Yeates wrote:

 On 29/10/2007, Ian Preston [EMAIL PROTECTED] wrote:
  G'day guys,
  I'm involved in the development of a very powerful and flexible grid
  software, which we plan to release in January. It is all java based.
  http://www-nereus.physics.ox.ac.uk/ (bear in mind you can't download
  it yet and the website is out of date)
 
  One of the things I'd like to do on it, once it is finished, is some
  kind of attack on Go. I've messed around trying to genetically
  generate algorithms to play go. However this has had to go on the back
  burner for the moment. The brief attempt I made had no way of storing
  data between games (I ran out of time) and the best algorithm it came
  up with was a purely random algorithm... :-)
 
  our group is also the one that is doing JPC - 
  http://www-jpc.physics.ox.ac.uk/
 
  I'd love to hear about anyone else distributed attacks on Go.
 
 It would be great to see a java port of GoTools by Thomas Wolf[1],
 which is probably the kind of thing that most naturally lends itself
 to distributed attacks.
 
 Does anyone know whether GoTools is under active development? The
 webpages were last updated in 2001...

There is a newer web page 
http://lie.math.brocku.ca/gotools/
with links to some recent publications for checking solutions of life and
death problems and a link to http://lie.math.brocku.ca/gotools/applet.html
which is a Java based online service. A standalone Java interface was
developed on and off and will hopefully be ready in the next months.

Thomas Wolf

 
 cheers
 stuart
 
 [1] http://www.qmw.ac.uk/~ugah006/gotools/
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Re: [computer-go] Amsterdam paper

[Thomas Wolf]

 On 5/19/07, Thomas Wolf [EMAIL PROTECTED] wrote:
  Here is another Amsterdam paper on Go, although about life  death
  and not full game playing.
 
 I may be missing the obvious, but in Section 4.2, Diagram 13,
 isn't Black 10 a basic ko violation?

Yes, that eats up one of the necessary external ko-threats 
which White needs in order to win. The GoLaTeX style file I have
does not mention the Ko-threats and answers played elsewhere
underneath the diagrams.

Thomas

 
 regards,
 -John
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[computer-go] Amsterdam paper

[Thomas Wolf]

Here is another Amsterdam paper on Go, although about life  death
and not full game playing.

http://lie.math.brocku.ca/twolf/papers/bugsintro.ps

Thomas
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Re: [computer-go] Why not forums?

[Thomas Wolf]

On Mon, 5 Feb 2007, Christoph Birk wrote:

    Why can't we use proper forums instead of this
   outdated list?
   Forums are easier to keep track of and search for
   messages. As a
   start we can use Yahoo groups. What do you think?
 
 I vote for keeping this (email) list.
 
 Christoph
 

I vote for keeping this (email) list too.

Thomas

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[computer-go] position

[Thomas Wolf]

About 2 months ago I sent a note to this email list about a research chair
position and a postdoc position, both financed through the SHACRNET High
Perfomance Consortium.  The advert below does not have high performance
computing as a requisite attached. But because it mentions discrete math,
combinatorics and experience in computation as valuable strengths, this might
be of interest to someone on the email list. Together with a few students we
already have a small but active computer Go group at our math department.

Thomas Wolf
Prof at Department of Mathematics
Brock University
Ontario, Canada

---

BROCK UNIVERSITY
FACULTY OF MATHEMATICS AND SCIENCES

MATHEMATICS

The Department of Mathematics invites applications for a tenure-track
appointment in an area of discrete and computational mathematics at the rank
of Assistant Professor starting July 1, 2007.

The Department offers an MSc in Mathematics and Statistics, has an innovative
and unique B.Sc. Mathematics program called MICA (Mathematics Integrated with
Computers and Applications) and plays a leading role in Mathematics Education.

The successful candidate must have a PhD in Mathematics or related field by
the time of the appointment, a proven record of or potential for research
excellence, and an active research program that will attract external
funding. Ideally, the candidate’s area of research would complement that of
current faculty.

The position requires undergraduate teaching including Combinatorics and
Mathematics for Computer Science, graduate teaching, and supervision of
graduate students. The successful candidate must demonstrate strong teaching
abilities and a committed interest in the use of technology for the
exploration, understanding and applications of mathematics.

The appointment is subject to the availability of funds. The review of
applications will start on February 28, 2007 and will continue until the
position is filled. Applicants should send a curriculum vitae, an outline of
their research plan and a description of teaching philosophies, and arrange
for at least three letters of reference (one of which should address teaching)
to be sent directly to:

Chair of the Mathematics Search Committee
Department of Mathematics
Brock University
St. Catharines, Ontario
L2S 3A1, Canada
E-mail: [EMAIL PROTECTED]

In accordance with Canadian Immigration requirements, priority will be given
to citizens and permanent residents of Canada. Brock University encourages
applications from all qualified individuals including women, members of
minorities, native people, and persons with disabilities.

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RE: [computer-go] Useless moves in the endgame

[Thomas Wolf]

On Tue, 9 Jan 2007, Chaslot G (MICC) wrote:

 Mango passes as soon as the opponent passes two times in a row.
 Might this lead to bugs in some situations?

You need 3 passes in case of ko.

Thomas

 
 Anyway this is very nice for playing against humans and GnuGo.
 
 Guillaume
 
 -Original Message-
 From: [EMAIL PROTECTED]
 [mailto:[EMAIL PROTECTED] On Behalf Of Benjamin
 Teuber
 Sent: Tuesday, January 09, 2007 4:32 PM
 To: computer-go
 Subject: [computer-go] Useless moves in the endgame
 
 I just lost my first game against MoGo on KGS, 9x9, 0.5 komi, I was
 white.
 Impressing!
 But as a human, you don't like the useless endgame-moves MC-programs 
 play against you when they know they win anyways.
 In order to make these programs more attractive for humans, I would like
 
 them to play the move winning by the biggest amount of points once 
 several moves have the same high winning probability at the endgame.
 What do you think about this?
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