Re: [Computer-go] Exploiting Cyclic Symmetry in Convolutional Neural Networks

[Steven Clark]

See also: Oriented Response Networks https://arxiv.org/abs/1701.01833

On Wed, Feb 28, 2018 at 11:40 AM, Jonathan Roy  wrote:

> I'm curious if anyone has applied this idea in their Go software, and what
> results you obtained? It is a way to make rotations (and transpositions
> with more effort) go away as an issue, regardless of the way you input the
> board you'd get the same result back out. Short summary from the paper (
> https://arxiv.org/pdf/1602.02660.pdf):
>
> We have introduced a framework for building rotation
> equivariant neural networks, using four new layers which
> can easily be inserted into existing network architectures.
> Beyond adapting the minibatch size used for training, no
> further modifications are required. We demonstrated improved
> performance of the resulting equivariant networks
> on datasets which exhibit full rotational symmetry, while
> reducing the number of parameters. A fast GPU implementation
> of the rolling operation for Theano (using
> CUDA kernels) is available at https://github.com/benanne/kaggle-ndsb.
>
> It was apparently used by this science competition winner:
>
> http://benanne.github.io/2015/03/17/plankton.html
>
> And there's related codebase here that implements the paper Group
> Equivariant Convolutional Networks (https://tacocohen.files.
> wordpress.com/2016/06/gcnn.pdf).
>
> https://github.com/tscohen/gconv_experiments
>
> The paper makes it sound like implementing for rotation would be straight
> forward, and implementing for transposition more difficult but also
> doable.Which sounds perfect for Go AI applications.
>
> -Jonathan
>
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Breakthrough: FineArt beating Ke Jie with 2 Handicap Stones

[Steven Clark]

Not according to this: http://eidogo.com/#EUexCx07
via reddit

On Sun, Jan 21, 2018 at 11:54 AM, Jim O'Flaherty  wrote:

> So the AI FineArt is assumed to have taken black with a two stone handicap?
>
> On Jan 21, 2018 9:41 AM, "Michael Alford"  wrote:
>
>> This is the problem we had with the link to the IGF article.  The sense
>> we made of the statement attributed to Ke Jie was he can't give FineArt h2
>> because it is as strong as he is.  Thanks, Ingo, for the iink to the
>> forum.  I have the games and will share them.
>>
>> Michael
>>
>> On 1/21/18 6:22 AM, Jim O'Flaherty wrote:
>>
>> It's unclear to me who played black with the two handicap stones.  Ke Jie
>> or FineArt?
>>
>> On Jan 21, 2018 1:56 AM, Ingo Althöfer <3-hirn-ver...@gmx.de> wrote:
>>
>>> Stefan Kaitschick posted this in the German computer go forun:
>>> http://www.dgob.de/yabbse/index.php?topic=6728.msg215694#msg215694
>>>
>>> His text roughly translated to English:
>>> > I downloaded FineArt free 2 stone games.
>>> > At least the game of Ke Jies is included.
>>> > Perhaps this is (essentially) the collection.
>>> > You have to rename the downloaded file to new ending .zip
>>>
>>> Hope this helps, Ingo.
>>>
>>>
>>> > Gesendet: Sonntag, 21. Januar 2018 um 05:49 Uhr
>>> > Von: "Michael Alford" 
>>> > An: computer-go@computer-go.org
>>> > Betreff: Re: [Computer-go] Breakthrough: FineArt beating Ke Jie with 2
>>> Handicap Stones
>>> >
>>> > Could someone make sgf's of these games available?
>>> ___
>>> Computer-go mailing list
>>> Computer-go@computer-go.org
>>> http://computer-go.org/mailman/listinfo/computer-go
>>
>>
>>
>> ___
>> Computer-go mailing 
>> listComputer-go@computer-go.orghttp://computer-go.org/mailman/listinfo/computer-go
>>
>>
>>
>> ___
>> Computer-go mailing list
>> Computer-go@computer-go.org
>> http://computer-go.org/mailman/listinfo/computer-go
>>
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Alphago and solving Go

[Steven Clark]

This is semantics. Yes, in the limit of infinite time, it is brute-force.
Meanwhile, in the real world, AlphaGo chooses to balance its finite time
budget between depth & width. The mere fact that the CNN policy network
generates a score for each coordinate on the board in a given position,
does not mean that all of those nodes will be expanded in any reasonable
scenario.

On Sun, Aug 6, 2017 at 2:20 PM, Brian Sheppard <sheppar...@aol.com> wrote:

> I understand why most people are saying that AlphaGo is not brute force,
> because it appears to be highly selective. But MCTS is a full width search.
> Read the AlphaGo papers, as one of the other respondents (rather
> sarcastically) suggested: AlphaGo will eventually search every move at
> every node.
>
>
>
> MCTS has the appearance of a selective search because time control
> terminates search while the tree is still ragged. In fact, it will search
> every continuation an infinite number of times.
>
>
>
> In order to have high performance, an MCTS implementation needs to search
> best moves as early as possible in each node. It is in this respect that
> AlphaGo truly excels. (AlphaGo also excels at whole board evaluation, but
> that is a separate topic.)
>
>
>
>
>
> *From:* Steven Clark [mailto:steven.p.cl...@gmail.com]
> *Sent:* Sunday, August 6, 2017 1:14 PM
> *To:* Brian Sheppard <sheppar...@aol.com>; computer-go <
> computer-go@computer-go.org>
> *Subject:* Re: [Computer-go] Alphago and solving Go
>
>
>
> Why do you say AlphaGo is brute-force? Brute force is defined as: "In
> computer science, brute-force search or exhaustive search, also known as
> generate and test, is a very general problem-solving technique that
> consists of *systematically enumerating all possible candidates* for the
> solution and checking whether each candidate satisfies the problem's
> statement."
>
>
>
> The whole point of the policy network is to avoid brute-force search, by
> reducing the branching factor...
>
>
>
> On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go <
> computer-go@computer-go.org> wrote:
>
> Yes, AlphaGo is brute force.
>
> No it is impossible to solve Go.
>
> Perfect play looks a lot like AlphaGo in that you would not be able to
> tell the difference. But I think that AlphaGo still has 0% win rate against
> perfect play.
>
>
>
> My own best guess is that top humans make about 12 errors per game. This
> is estimated based on the win rate of top pros in head-to-head games. The
> calculation starts by assuming that Go is a win at 6.5 komi for either
> Black (more likely) or White, so a perfect player would win 100% for Black.
> Actual championship caliber players win 51% to 52% for Black. In 9-dan play
> overall, I think the rate is 53% to 54% for Black. Then you can estimate
> how many errors each player has to make to bring about such a result. E.g.,
> If players made only one error on average, then Black would win the vast
> majority of games, so they must make more errors. I came up with 12 errors
> per game, but you can reasonably get other numbers based on your model.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
> Behalf Of *Cai Gengyang
> *Sent:* Sunday, August 6, 2017 9:49 AM
> *To:* computer-go@computer-go.org
> *Subject:* [Computer-go] Alphago and solving Go
>
>
>
> Is Alphago brute force search?
>
> Is it possible to solve Go for 19x19 ?
>
> And what does perfect play in Go look like?
>
> How far are current top pros from perfect play?
>
>
>
> Gengyang
>
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
>
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Alphago and solving Go

[Steven Clark]

Why do you say AlphaGo is brute-force? Brute force is defined as: "In
computer science, brute-force search or exhaustive search, also known as
generate and test, is a very general problem-solving technique that
consists of *systematically enumerating all possible candidates* for the
solution and checking whether each candidate satisfies the problem's
statement."

The whole point of the policy network is to avoid brute-force search, by
reducing the branching factor...

On Sun, Aug 6, 2017 at 10:42 AM, Brian Sheppard via Computer-go <
computer-go@computer-go.org> wrote:

> Yes, AlphaGo is brute force.
>
> No it is impossible to solve Go.
>
> Perfect play looks a lot like AlphaGo in that you would not be able to
> tell the difference. But I think that AlphaGo still has 0% win rate against
> perfect play.
>
>
>
> My own best guess is that top humans make about 12 errors per game. This
> is estimated based on the win rate of top pros in head-to-head games. The
> calculation starts by assuming that Go is a win at 6.5 komi for either
> Black (more likely) or White, so a perfect player would win 100% for Black.
> Actual championship caliber players win 51% to 52% for Black. In 9-dan play
> overall, I think the rate is 53% to 54% for Black. Then you can estimate
> how many errors each player has to make to bring about such a result. E.g.,
> If players made only one error on average, then Black would win the vast
> majority of games, so they must make more errors. I came up with 12 errors
> per game, but you can reasonably get other numbers based on your model.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
> Behalf Of *Cai Gengyang
> *Sent:* Sunday, August 6, 2017 9:49 AM
> *To:* computer-go@computer-go.org
> *Subject:* [Computer-go] Alphago and solving Go
>
>
>
> Is Alphago brute force search?
>
> Is it possible to solve Go for 19x19 ?
>
> And what does perfect play in Go look like?
>
> How far are current top pros from perfect play?
>
>
>
> Gengyang
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Alphago and solving Go

[Steven Clark]

No (have you read any of the papers about it?)
No
We don't know
We don't know (pros used to claim they were 2-3 stones away from God, but
AlphaGo might have encouraged them to be a bit more humble)

On Sun, Aug 6, 2017 at 9:49 AM, Cai Gengyang  wrote:

> Is Alphago brute force search?
> Is it possible to solve Go for 19x19 ?
> And what does perfect play in Go look like?
> How far are current top pros from perfect play?
>
> Gengyang
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] AlphaGo selfplay 3 games

[Steven Clark]

My interpretation is that all variations are from AlphaGo, whereas the
human pros are just weighing in on those. Hence you will see the pros
"approve of this variation" or "express doubts" about it.

On Thu, Sep 15, 2016 at 11:04 AM, Darren Cook  wrote:

> > DeepMind published AlphaGo's selfplay 3 games with comment.
>
> I've just been playing through the AlphaGo-Lee first game. When it shows
> a variation, is this what AlphaGo was expecting, i.e. its prime
> variation? Or is the follow-up "just" the opinion of the pro commentators?
>
> (E.g. game 1, move 13, the keima; the commentary says "While will
> attach..." Can I read that as meaning this is the move AlphaGo would
> have chosen if black had played there?)
>
> Thanks,
> Darren
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Go Aesthetics

[Steven Clark]

It's an inherently subjective thought-exercise -- ask 10 different players
and you will get 10 different ideas of what constitutes beauty. I'm not
even sure I agree with the metrics proposed in
http://www.wseas.us/e-library/transactions/computers/2008/26-184.pdf for
chess -- why is it inherently more "beautiful" to use a weaker piece as
opposed to a stronger piece?

In go, there are a lot of characteristics that exist on a continuum (e.g.
aggression vs. calm/steady, etc.) Play at either end of the spectrum has
its own appeal.
Metrics one could analyze:
-Willingness to tenuki
-Ability to maintain sente
-Tenacity of attack (how to measure?)
-Efficiency of shape (how to measure?)
-Favoring influence vs. territory
-Preference for invasion vs. reduction
etc.

One would do better to analyze a given player over many games, vs. just
looking at one game (since there is such variability).

On Mon, Jan 11, 2016 at 10:10 PM, Gonçalo Mendes Ferreira 
wrote:

> Hi, some time back I mentioned creating a program that evaluates the
> aesthetics of a game of Go. Has anyone given it some thought? I'd love to
> have a comparison between professional and amateur dan matches, or across
> time periods or players. There are a few papers on aesthetics for chess so
> I don't see why not Go. It shouldn't be terribly difficult to make, after
> deciding on the things to look for. I'd like to kickstart this discussion.
>
> For reference:
> Advanced Computer Recognition of Aesthetics in the Game of Chess by Azlan
> Iqbal and Mashkuri Yaacob
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Standard Computer Go Datasets - Proposal

[Steven Clark]

To answer the original question: yes, the curation of a dataset like this
would be hugely beneficial to the community. Look at what ImageNet has done
for computer vision. In fact, it might be good to emulate ImageNet further
and pre-split the dataset into a publicly-available training set, and a
hidden testing set, for truly objective comparisons between move-prediction
algorithms.

If you undertake this, many thanks in advance!

On Fri, Nov 13, 2015 at 1:20 PM, Dave Dyer  wrote:

>
> I was recently working on assigning final scores to completed games, using
> the large data set from Badukmovies.com.
>
> My observation is that the size of the data set (50,000 games) is not
> large enough to get good coverage of unusual situations occurring in real
> games.
>
> There's a definite need for a curated collection of atypical but
> interesting games, probably manipulated to explore the boundaries
> between interesting and normal.
>
> ___
> Computer-go mailing list
> Computer-go@computer-go.org
> http://computer-go.org/mailman/listinfo/computer-go
>
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Mental Imagery in Go - playlist

[Steven Clark]

RE: CNNs: They can be, and have been, successfully applied to movies as
well. See http://www.cs.cmu.edu/~rahuls/pub/cvpr2014-deepvideo-rahuls.pdf
Also, in the first .pdf I linked you, the input layer has a notion of age
of the stones. For example, this stone was played 5 moves ago, this one 3
moves ago, etc. So, it is not a strictly static snapshot of a board.
In any event, the best performance will probably not come ONLY from CNNs
(although its prediction accuracy is surprisingly high), but the marriage
of CNNs to monte-carlo tree search, etc.

My sense is that we will continue clinging to romantic notions of human
intelligence (shapes, proverbs, etc.) until we eventually get ground to
dust in a Deep-Blue style competition. Not too long now :)

On Sun, Aug 2, 2015 at 9:33 PM, djhbrown . djhbr...@gmail.com wrote:

 Thanks for the replies to my first message; i looked at the links you
 supplied and comment on them later in this email.

 I noticed that Google does not show you the playlist when you look at
 episode 1 of the series (of currently 3 videos), so you may have missed the
 second two episodes which are more significant than the first.  Here is a
 link to the playlist:

 https://www.youtube.com/playlist?list=PL4y5WtsvtduqNW0AKlSsOdea3Hl1X_v-S

 episode 2 introduces mental images and episode 3 is a conversation between
 Hajin Lee and me about her thoughts on a couple of moves early in one of
 her games.  It includes my first attempt at picturing her thoughts, both
 as symbolic information structures and as paint overlays on the game board.

 My hope is that the former might one day become the basis of symbolic
 generic heuristic rules that could be used to generate and evaluate move
 candidates and the latter could evolve into useful instructional materials
 for people learning the game - so that they can, so to speak, look through
 the eyes of an expert like Hajin.

 To these ends, i need the assistance of people with better skills than me
 at (a) drawing pictures, (b) software and (c) Go.  I think that programming
 is like gymnastics - best done by the young, with their abundance of
 enthusiasm and energy.  I enjoyed programming 50 years ago, but i'm too old
 in the tooth now to burn midnight oil.

 Now to your replies:

 Folkert: Stop is a good start but as you already know, there's a long
 way to go yet :)

 Steven:  I expect there is a future for CNN's in recognising static
 images, but my gut feel is that a position in a Go game is more like one
 frame of a movie; as such, it requires a technology that can interpret
 dynamic images - maybe work being done in automatous car driving can
 contribute something useful to Go playing?  Nevertheless, I was surprised
 by the many humanlike moves of DCNNigo on KGS (until it revealed its
 brittleness).  To be sure, drawing upon the moves of experts is one way of
 gaining expertise, but my feeling is that one should try to abstract the
 position - to generalise from the examples - so that general knowledge can
 be formed and applied to novel situations.  It may be that a CNN arguably
 does do some kind of generalisation - but can it, for example, characterise
 something as basic as the waist of a keima?

 Ingo:  Tanja may be the kind of artist who could produce nice drawings of
 Hajin's mental images, perhaps based on my own crude sketches?  It would be
 unpaid work though...  I liked Fuego's and Jonathan's territory pictures,
 which reminded me of Zobrist's early work on computing influence.  [Albert
 Zobrist (*1969*). *A Model of Visual Organisation for the Game of Go*.
 Proceedings of the Spring Joint Computer Conference, Vol. 34, pp. 103-112.]
 However, whereas being able to picture influence and territory is one of my
 objectives, i want to try to picture the richness of what Hajin (aka
 Haylee) sees rather than the result of a primitive computation.  For
 example, at 10:24 in episode 3, she points out that when black is on J4
 instead of K4, there is an opening in black's lower side for white to
 invade.  This tiny gap makes all the difference to the dynamic meaning of
 the position a few moves prior (ie whether it is sensible for white to
 approach Q3 at Q5).

 One of the major influences on my own thinking about Go programming is the
 seminal work Thought and Choice in Chess by Adriaan de Groot  which i
 reckon is well worth a read by anyone interested in programming Go
 https://books.google.com.au/books?id=b2G1CRfNqFYCpg=PA99

 ---
 ​personal website http://sites.google.com/site/djhbrown2/home







 ___
 Computer-go mailing list
 Computer-go@computer-go.org
 http://computer-go.org/mailman/listinfo/computer-go

___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Fwd: mental imagery in Go

[Steven Clark]

Not sure how deep down the rabbit hole you want to go, but you might want
to take a look at convolutional neural networks and their applicability to
go, e.g.: http://arxiv.org/abs/1412.6564

They are used ubiquitously for image classification and object detection,
but people are looking at tying them to Go as well. They have some analogs
to biological image processing (gabor filters, etc.), but are not 1-to-1.
The tricky thing with respect to visualization, is that while
visualizations of lower-level layers in the network may make some sense,
higher-level layers don't visualize as nicely. Take a look at NVIDIA's
DIGITS project to see how they are visualizing.

On Sat, Aug 1, 2015 at 12:46 AM, djhbrown . djhbr...@gmail.com wrote:

 i'm looking for people with expertise in
 ​art, ​
 computer graphics
 ​,​
 and/or AI software to help me make a video series about mental imagery in
 Go, partly with a view to expressing mental images in the form of heuristic
 rules that a program could use to generate and evaluate candidate moves.

 any takers?

 https://www.youtube.com/watch?v=Om_CVAevU8o

 --
 ​personal website http://sites.google.com/site/djhbrown2/home

 ___
 Computer-go mailing list
 Computer-go@computer-go.org
 http://computer-go.org/mailman/listinfo/computer-go

___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Using GPUs?

[Steven Clark]

Here are the papers I was thinking of:

http://arxiv.org/abs/1412.6564 (nvidia gtx titan black)
http://arxiv.org/abs/1412.3409 (nvidia gtx 780)

On Fri, Jun 26, 2015 at 2:09 AM, Nikos Papachristou nikp...@gmail.com
wrote:

 Not go related, but you may find this deep learning GPU hardware guide
 useful:
 https://timdettmers.wordpress.com/2015/03/09/deep-learning-hardware-guide/

 As for hardware breakthroughs, Nvidia has announced that its next
 generation GPUs (codenamed Pascal) will offer 10x the performance in 2016,
 so you might want to wait a little more.

 Nikos

 On Thu, Jun 25, 2015 at 8:18 PM, Darren Cook dar...@dcook.org wrote:

 I wondered if any of the current go programs are using GPUs.

 If yes, what is good to look for in a GPU? Links to essential reading on
 this topic would be welcome. (*)

 If not, is there some hardware breakthrough being waited for, or some
 algorithmic one?

 Darren

 *: After many years of being happy with built-in graphics, I'm now
 thinking to get a gaming PC, to show off some WebGL data
 visualizations. Assuming the cost is in the same ballpark, I thought I'd
 get one that would allow some scientific computing experiments too.
 ___
 Computer-go mailing list
 Computer-go@computer-go.org
 http://computer-go.org/mailman/listinfo/computer-go



 ___
 Computer-go mailing list
 Computer-go@computer-go.org
 http://computer-go.org/mailman/listinfo/computer-go

___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [Computer-go] Using GPUs?

[Steven Clark]

Can't speak to current go programs, but there's lots of exciting stuff
going on currently with machine learning / deep neural networks, most of
which uses GPUs heavily. I know some research has been done on
convolutional neural networks for Go -- don't have any links handy at the
moment though.

Recommend getting a recent vintage NVIDIA gpu (for CUDA support). Say, a
780 or 980. Either of these would be fine for your visualization purposes
as well.

On Thu, Jun 25, 2015 at 1:18 PM, Darren Cook dar...@dcook.org wrote:

 I wondered if any of the current go programs are using GPUs.

 If yes, what is good to look for in a GPU? Links to essential reading on
 this topic would be welcome. (*)

 If not, is there some hardware breakthrough being waited for, or some
 algorithmic one?

 Darren

 *: After many years of being happy with built-in graphics, I'm now
 thinking to get a gaming PC, to show off some WebGL data
 visualizations. Assuming the cost is in the same ballpark, I thought I'd
 get one that would allow some scientific computing experiments too.
 ___
 Computer-go mailing list
 Computer-go@computer-go.org
 http://computer-go.org/mailman/listinfo/computer-go
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go

Re: [computer-go] open source Go AI's written in pure python

[Steven Clark]

We'll be the judges of that niceelegant bit ;)

I think using the ease of python to get started with algorithms and then
later pushing the performance critical sections to C and wrapping with SWIG
is a great idea.

On 5/25/07, Eduardo Sabbatella [EMAIL PROTECTED] wrote:


No, but soon I will publish to the public a Java Go
engine including a nice and elegant set of go base
classes.

--- George Dahl [EMAIL PROTECTED] escribió:

 Does anyone know of any open source Go AI's written
 in pure python?

 Thanks,
 George
  ___
 computer-go mailing list
 computer-go@computer-go.org

http://www.computer-go.org/mailman/listinfo/computer-go/



  __
Preguntá. Respondé. Descubrí.
Todo lo que querías saber, y lo que ni imaginabas,
está en Yahoo! Respuestas (Beta).
¡Probalo ya!
http://www.yahoo.com.ar/respuestas

___
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/

___
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/

Re: [computer-go] post

[Steven Clark]

For true beginners:
http://playgo.to/interactive/

Janice Kim's 5 book series at http://samarkand.net/

Or play online (KGS has a good english community): http://www.gokgs.com/


On 12/2/06, Mike Olsson [EMAIL PROTECTED] wrote:


I am looking for tutorials and articles on the web to learn go. Would you
please direct me to these resources if possible.

Thank you

--
Check out the all-new Yahoo! Mail 
betahttp://us.rd.yahoo.com/evt=43257/*http://advision.webevents.yahoo.com/mailbeta-
 Fire up a more powerful email and get things done faster.


___
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/


___
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/