Re: [Computer-go] AlphaZero tensorflow implementation/tutorial

[Xavier Combelle]

looks you made it work on a 7x7 19x19 would probably give better result
especially against yourself if you are a complete novice

for not cheating against gnugo, use --play-out-aftermath of gnugo parameter

If I don't mistake a competitive ai would need a lot more training such
what does leela zero https://github.com/gcp/leela-zero

Le 10/12/2018 à 01:25, cody2007 via Computer-go a écrit :
> Hi all,
>
> I've posted an implementation of the AlphaZero algorithm and brief
> tutorial. The code runs on a single GPU. While performance is not that
> great, I suspect its mostly been limited by hardware limitations (my
> training and evaluation has been on a single Titan X). The network can
> beat GNU go about 50% of the time, although it "abuses" the scoring a
> little bit--which I talk a little more about in the article:
>
> https://medium.com/@cody2007.2/alphazero-implementation-and-tutorial-f4324d65fdfc
>
> -Cody
>
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Re: [Computer-go] 9x9 is last frontier?

[Xavier Combelle]

Where is leela chess. How many games it is trained on?

Le 2 mars 2018 18:20, "Dan"  a écrit :

> Hello Aja,
>
> Could you enlighten me on how AlphaZero handles tactics in chess ?
>
> It seems the mcts approach as described in the paper does not perform well
> enough.
>
> Leela-chess is not performing well enough even though leela-go seems to be
> doing well.
>
> Daniel
>
>
>
>
>
> On Fri, Mar 2, 2018 at 4:52 AM, Aja Huang  wrote:
>
>>
>>
>> 2018-03-02 6:50 GMT+00:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:
>>
>>> Von: "David Doshay" 
>>> > Go is hard.
>>> > Programming is hard.
>>> >
>>> > Programming Go is hard squared.
>>> > ;^)
>>>
>>> And that on square boards.
>>> Mama mia!
>>>
>>
>> Go is hard for humans, but in my own opinion I think Go seems to be too
>> easy for deep learning. So is programming Go now. :)
>>
>> Aja
>>
>>
>>>
>>> ;-) Ingo.
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Re: [Computer-go] sgf file for recent handicap games of pros vs programs

[Xavier Combelle]

Tygem files are not sgf but gib:here dome convertor
https://senseis.xmp.net/?GIB

Le 23 janv. 2018 06:21, "Ray Tayek"  a écrit :

> is anyone collecting the sgf file for these games?
>
> i get the stuff below when i try to download.
>
> thanks
>
>
> C:\Users\ray\Downloads>od -c FineArt_A-2hcp.sgf | head
> 000   P   K 003 004 024  \0  \0  \0  \0  \0   < 005   5 L  \0  \0
> 020  \0  \0  \0  \0  \0  \0  \0  \0  \0  \0 017  \0  \0  \0 F   i
> 040   n   e   A   r   t   _   A   -   2   h   c   p   /   P K 003
> 060 004 024  \0  \0  \b  \b  \0   t 004   5   L   L   9   ] 233  \t
> 100 002  \0  \0 227 004  \0  \0   A  \0  \0  \0   F   i   n e   A
> 120   r   t   _   A   -   2   h   c   p   /   [   8   8   8 8   8
> 140   8   8   8   ]   v   s   [ 347 273 235 350 211 272 346 214 207
> 160 345 257 274   A   ]   1   5   1   6   0   7   2   2   0 8   0
> 200   1   0   0   0   1   1   6   2   .   s   g   f   E 222 315 216
> 220 323   0 024 205   _ 245   K 020 032 224 314   @ 325 241 253 026
>
> --
> Honesty is a very expensive gift. So, don't expect it from cheap people -
> Warren Buffett
> http://tayek.com/
>
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Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

[Xavier Combelle]

Another result is that chess is really drawish, at the opposite of shogi


Le 06/12/2017 à 18:50, Richard Lorentz a écrit :
> One chess result stood out for me, namely, just how much easier it was
> for AlphaZero to win with white (25 wins, 25 draws, 0 losses) rather
> than with black (3 wins, 47 draws, 0 losses).
>
> Maybe we should not give up on the idea of White to play and win in chess!
>
> On 12/06/2017 01:24 AM, Hiroshi Yamashita wrote:
>> Hi,
>>
>> DeepMind makes strongest Chess and Shogi programs with AlphaGo Zero
>> method.
>>
>> Mastering Chess and Shogi by Self-Play with a General Reinforcement
>> Learning Algorithm
>> https://urldefense.proofpoint.com/v2/url?u=https-3A__arxiv.org_pdf_1712.01815.pdf=DwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=w0qxE9GOfBVzqPOT0NBm1nsdQqJMlNu40BOCWfsO-gQ=dsola-9J77ArHVeuVc0ZCZKn2nJOsjfsnJzPc_MdPDo=
>>
>>
>> AlphaZero(Chess) outperformed Stockfish after 4 hours,
>> AlphaZero(Shogi) outperformed elmo after 2 hours.
>>
>> Search is MCTS.
>> AlphaZero(Chess) searches 80,000 positions/sec.
>> Stockfish    searches 70,000,000 positions/sec.
>> AlphaZero(Shogi) searches 40,000 positions/sec.
>> elmo searches 35,000,000 positions/sec.
>>
>> Thanks,
>> Hiroshi Yamashita
>>
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>
>
>
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Re: [Computer-go] Significance of resignation in AGZ

[Xavier Combelle]

It might make sense to enable resignation threshold even on stupid
level. As such the first thing the network should learn would be not to
resign to early (even before not passing)


Le 02/12/2017 à 18:17, Brian Sheppard via Computer-go a écrit :
>
> I have some hard data now. My network’s initial training reached the
> same performance in half the iterations. That is, the steepness of
> skill gain in the first day of training was twice as great when I
> avoided training on fill-ins.
>
>  
>
> The has all the usual caveats: only one run before/after, YMMV, etc.
>
>  
>
> *From:*Brian Sheppard [mailto:sheppar...@aol.com]
> *Sent:* Friday, December 1, 2017 5:39 PM
> *To:* 'computer-go' 
> *Subject:* RE: [Computer-go] Significance of resignation in AGZ
>
>  
>
> I didn’t measure precisely because as soon as I saw the training
> artifacts I changed the code. And I am not doing an AGZ-style
> experiment, so there are differences for sure. So I will give you a swag…
>
>  
>
> Speed difference is maybe 20%-ish for 9x9 games.
>
>  
>
> A frequentist approach will overstate the frequency of fill-in plays
> by a pretty large factor, because fill-in plays are guaranteed to
> occur in every game but are not best in the competitive part of the
> game. This will affect the speed of learning in the early going.
>
>  
>
> The network will use some fraction (almost certainly <= 20%) of its
> capacity to improve accuracy on positions that will not contribute to
> its ultimate strength. This applies to both ordering and evaluation
> aspects.
>
>  
>
>  
>
>  
>
>  
>
> *From:*Andy [mailto:andy.olsen...@gmail.com]
> *Sent:* Friday, December 1, 2017 4:55 PM
> *To:* Brian Sheppard ; computer-go
> 
> *Subject:* Re: [Computer-go] Significance of resignation in AGZ
>
>  
>
> Brian, do you have any experiments showing what kind of impact it has?
> It sounds like you have tried both with and without your ad hoc first
> pass approach?
>
>  
>
>  
>
>  
>
>  
>
> 2017-12-01 15:29 GMT-06:00 Brian Sheppard via Computer-go
> >:
>
> I have concluded that AGZ's policy of resigning "lost" games early
> is somewhat significant. Not as significant as using residual
> networks, for sure, but you wouldn't want to go without these
> advantages.
>
> The benefit cited in the paper is speed. Certainly a factor. I see
> two other advantages.
>
> First is that training does not include the "fill in" portion of
> the game, where every move is low value. I see a specific effect
> on the move ordering system, since it is based on frequency. By
> eliminating training on fill-ins, the prioritization function will
> not be biased toward moves that are not relevant to strong play.
> (That is, there are a lot of fill-in moves, which are usually not
> best in the interesting portion of the game, but occur a lot if
> the game is played out to the end, and therefore the move
> prioritization system would predict them more often.) My ad hoc
> alternative is to not train on positions after the first pass in a
> game. (Note that this does not qualify as "zero knowledge", but
> that is OK with me since I am not trying to reproduce AGZ.)
>
> Second is the positional evaluation is not training on situations
> where everything is decided, so less of the NN capacity is devoted
> to situations in which nothing can be gained.
>
> As always, YMMV.
>
> Best,
> Brian
>
>
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>  
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>
>
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Re: [Computer-go] Learning related stuff

[Xavier Combelle]

Le 21/11/2017 à 23:27, "Ingo Althöfer" a écrit :
> Hi Erik,
>
>> No need for AlphaGo hardware to find out; any 
>> toy problem will suffice to explore different 
>> initialization schemes... 
> I know that. 
>
> My intention with the question is a different one:
> I am thinking how humans are learning. Is it beneficial
> to have learnt related - but different - stuff before?
> The answer will depend on the case, of course.
>
> And in my role as a voyeur, I want to understand if having
> learnt a Go variant X before turning my interest to a
> "slightly" different Go variant Y. Do, I want to combine
> the subject with some entertaining learning process.
> (For instance, looking at the AlphaGo Zero games from the
> 72 h experiment in steps of 2 hours was not only insightful
> but also entertaining.)
>
>> you typically want to start with small weights so 
>> that the initial mapping is relatively smooth.
> But again: For instance, when a eight year old child starts
> to play violin, is it helpful or not when it had played
> say a trumpet before?
I believe that Human brain is too far from the alphago neural network
that one knowledge about one can be transfered to the other.
> My understanding is that the AlphaGo hardware is standing 
> somewhere in London, idle and waitung for new action...
Definitely not idle:
“[They] needed the computers for something else.”

source:
https://techcrunch.com/2017/11/02/deepmind-has-yet-to-find-out-how-smart-its-alphago-zero-ai-could-be/
> Ingo.
>
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Re: [Computer-go] Is MCTS needed?

[Xavier Combelle]

As far as I know, the state of art in chess is some flavor of alphabeta
(as long as I read stockfish source correctly),
so basically they prove their current esimation is the best one up to a
certain depth.

MCTS has the benefit to enable various depth search depending on how
good the evaluation is.
I believe that related to the way alpha go and alphago zero is, it is
far superior to alphabeta.



Le 16/11/2017 à 16:43, Petr Baudis a écrit :
>   Hi,
>
>   when explaining AlphaGo Zero to a machine learning audience yesterday
>
>   
> (https://docs.google.com/presentation/d/1VIueYgFciGr9pxiGmoQyUQ088Ca4ouvEFDPoWpRO4oQ/view)
>
> it occurred to me that using MCTS in this setup is actually such
> a kludge!
>
>   Originally, we used MCTS because with the repeated simulations,
> we would be improving the accuracy of the arm reward estimates.  MCTS
> policies assume stationary distributions, which is violated every time
> we expand the tree, but it's an okay tradeoff if all you feed into the
> tree are rewards in the form of just Bernoulli trials.  Moreover, you
> could argue evaluations are somewhat monotonic with increasing node
> depths as you are basically just fixing a growing prefix of the MC
> simulation.
>
>   But now, we expand the nodes literally all the time, breaking the
> stationarity possibly in drastic ways.  There are no reevaluations that
> would improve your estimate.  The input isn't binary but an estimate in
> a continuous space.  Suddenly the Multi-armed Bandit analogy loses a lot
> of ground.
>
>   Therefore, can't we take the next step, and do away with MCTS?  Is
> there a theoretical viewpoint from which it still makes sense as the best
> policy improvement operator?
>
>   What would you say is the current state-of-art game tree search for
> chess?  That's a very unfamiliar world for me, to be honest all I really
> know is MCTS...
>

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Re: [Computer-go] Nochi: Slightly successful AlphaGo Zero replication

[Xavier Combelle]

You make me really curious,
what is a Keras model ?

Le 10/11/2017 à 01:47, Petr Baudis a écrit :
>   Hi,
>
>   I got first *somewhat* positive results in my attempt to reproduce
> AlphaGo Zero - 25% winrate against GNUGo on the easiest reasonable task
> - 7x7 board. :)  a.k.a.
>
>   "Sometimes beating GNUGo on a tiny board" without human knowledge
>
> (much wow!)
>
>   Normally this would be a pretty weak result much but (A) I wanted to
> help calibrate other efforts on larger boards that are possibly still
> at the "random" stage, and (B) I'll probably move on to other projects
> again soon, so this might be as good as it gets for me.
>
>   I started the project by replacing MC simulations with a Keras model
> in my 550-line educational Go program Michi - it lived in its `nnet`
> branch until now when I separated it to a project on its own:
>
>   https://github.com/rossumai/nochi
>
> Starting from a small base means that the codebase is tiny and should be
> easy to follow, though it's not at all as tidy as Michi is.
>
> You can grab the current training state (== pickled archive of selfplay
> positions used for replay, chronological) and neural network weights
> from the github's "Releases" page:
>
>   https://github.com/rossumai/nochi/releases/tag/G171107T013304_00150
>
>   This is a truly "zero-knowledge" system like AlphaGo Zero - it needs
> no supervision, and it contains no Monte Carlo simulations or other
> heuristics. But it's not entirely 1:1, I did some tweaks which I thought
> might help early convergence:
>
>   * AlphaGo used 19 resnet layers for 19x19, so I used 7 layers for 7x7.
>   * The neural network is updated after _every_ game, _twice_, on _all_
> positions plus 64 randomly sampled positions from the entire history,
> this all done four times - on original position and the three
> symmetry flips (but I was too lazy to implement 90\deg rotation).
>   * Instead of supplying last 8 positions as the network input I feed
> just the last position plus two indicator matrices showing
> the location of the last and second-to-last move.
>   * No symmetry pruning during tree search.
>   * Value function is trained with cross-entropy rather than MSE,
> no L2 regularization, and plain Adam rather than hand-tuned SGD (but
> the annealing is reset time by time due to manual restarts of the
> script from a checkpoint).
>   * No resign auto-threshold but it is important to play 25% games
> without resigning to escale local "optima".
>   * 1/Temperature is 2 for first three moves.
>   * Initially I used 1000 "simulations" per move, but by mistake, last
> 1500 games when the network improved significantly (see below) were
> run with 2000 simulations per move.  So that might matter.
>
>   This has been running for two weeks, self-playing 8500 games.  A week
> ago its moves already looked a bit natural but it was stuck in various
> local optima.  Three days ago it has beaten GNUGo once across 20 games.
> Now five times across 20 games - so I'll let it self-play a little longer
> as it might surpass GNUGo quickly at this point?  Also this late
> improvement coincides with the increased simulation number.
>
>   At the same time, Nochi supports supervised training (with the rest
> kept the same) which I'm now experimenting with on 19x19.
>
>   Happy training,
>

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[Computer-go] Did some zero project already show improvement over random moves

[Xavier Combelle]

I wonder if some of zero project (project based on alphago zero paper)
that if I understood well was launched
did already had gather some kind of mesurable succeed, even very only of
the order of hundreds points.
If I understand correctly, the previous mails, the computation power you
have is 1700 less than the one of google.
So, as alphago zero goes approx 1000 elo points on 5 hours (just a
rought mesurement on nature paper graph), one could have 2.8 ELO points
won by day.

Xavier Combelle

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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

You are totally right it is not the same curves. according to the reddit
post.

So I was totally wrong

> On 27-10-17 10:15, Xavier Combelle wrote:
>> Maybe I'm wrong but both curves for alphago zero looks pretty similar
>> except than the figure 3 is the zoom in of figure 6
> The blue curve in figure 3 is flat at around 60 hours (2.5 days). In
> figure 6, at 2.5 days the line is near vertical. So it is not a zoom.
>
> Maybe this can help you:
> https://www.reddit.com/r/baduk/comments/77hr3b/elo_table_of_alphago_zero_selfplay_games/
>
> Note the huge Elo advantage of the 20 blocks version early on (it can
> learn faster, but stalls out faster).
>

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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

OK I will reread it attentively


Le 27/10/2017 à 19:19, Hideki Kato a écrit :
> Please read _through_ the paper sequentially.
> #I don't have enough skill to describe the reason because 
> it's not a technical but language issue.
>
> Hideki
>
>> I don't understand which element makes you say that
>> section 2 and 3 are all for a 20 block instance
>>
>>
>> Le 27/10/2017 E01:49, Hideki Kato a écrit :
>>> The 40 block version (2nd instance) first appeared in 
>>> Section 4 in the paper.  Section 2 and 3 are all for the 1st 
>>> instance.
>>>
>>> Hideki
>>>
>>> Xavier Combelle: <39a79a0e-7c7d-2a01-a2ae-573cda8b1...@gmail.com>:
>>>> Unless I mistake figure 3 shows the plot of supervised learning to
>>>> reinforcement learning, not 20 bloc/40 block
>>>> For searching mention of the 20 blocks I search for 20 in the whole
>>>> paper and did not found any other mention
>>>> than of the kifu thing.
>>>> Le 26/10/2017 E15:10, Gian-Carlo Pascutto a écrit :
>>>>> On 26-10-17 10:55, Xavier Combelle wrote:
>>>>>> It is just wild guesses  based on reasonable arguments but without
>>>>>> evidence.
>>>>> David Silver said they used 40 layers for AlphaGo Master. That's more
>>>>> evidence than there is for the opposite argument that you are trying to
>>>>> make. The paper certainly doesn't talk about a "small" and a "big" 
>>>> Master.
>>>>> You seem to be arguing from a bunch of misreadings and
>>>>> misunderstandings. For example, Figure 3 in the paper shows the Elo 
>> plot
>>>>> for the 20 block/40 layer version, and it compares to Alpha Go Lee, not
>>>>> Alpha Go Master. The Alpha Go Master line would be above the flattening
>>>>> part of the 20 block/40 layer AlphaGo Zero. I guess you missed this 
>> when
>>>>> you say that they "only mention it to compare on kifu prediction"?
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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

Maybe I'm wrong but both curves for alphago zero looks pretty similar
except than the figure 3 is the zoom in of figure 6

Le 27 oct. 2017 04:31, "Gian-Carlo Pascutto" <g...@sjeng.org> a écrit :

> Figure 6 has the same graph as Figure 3 but for 40 blocks. You can compare
> the Elo.
>
> On Thu, Oct 26, 2017, 23:35 Xavier Combelle <xavier.combe...@gmail.com>
> wrote:
>
>> Unless I mistake figure 3 shows the plot of supervised learning to
>> reinforcement learning, not 20 bloc/40 block
>>
>> For searching mention of the 20 blocks I search for 20 in the whole
>> paper and did not found any other mention
>>
>> than of the kifu thing.
>>
>>
>> Le 26/10/2017 à 15:10, Gian-Carlo Pascutto a écrit :
>> > On 26-10-17 10:55, Xavier Combelle wrote:
>> >> It is just wild guesses  based on reasonable arguments but without
>> >> evidence.
>> > David Silver said they used 40 layers for AlphaGo Master. That's more
>> > evidence than there is for the opposite argument that you are trying to
>> > make. The paper certainly doesn't talk about a "small" and a "big"
>> Master.
>> >
>> > You seem to be arguing from a bunch of misreadings and
>> > misunderstandings. For example, Figure 3 in the paper shows the Elo plot
>> > for the 20 block/40 layer version, and it compares to Alpha Go Lee, not
>> > Alpha Go Master. The Alpha Go Master line would be above the flattening
>> > part of the 20 block/40 layer AlphaGo Zero. I guess you missed this when
>> > you say that they "only mention it to compare on kifu prediction"?
>> >
>>
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>
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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Xavier Combelle]

what are semantic genetic algorithm ?

to my knowledge genetic algorithm lead to poor result except as a
metaheuristic in optimisation problem


Le 26/10/2017 à 14:40, Jim O'Flaherty a écrit :
> When I get time to spend dozens of hours on computer go again, I plan
> to play in Robert's area with semantic genetic algorithms. I am an
> Architect Software Engineer. Robert's work will allow me better than
> starting entirely from random in much the same way AlphaGo
> bootstrapped from the 100K of professional games. AG0 then leveraged
> AlphaGo in knowing an architecture that was close enough. My intuition
> is my approach will be something similar in it's evolution.
>
> This is the way we're going to "automate" creating provided proofing
> of human cognition styled computer go players to assist humans in a
> gradient ascent learning cycle.
>
> So, Robert, I admire and am encouraged by your research for my own
> computer go projects in this area. Keep kicking butt in your unique
> way. We are in an interesting transition in this community. Stick it
> out. It will be worth it long term.
>
> On Oct 26, 2017 4:38 AM, "Petri Pitkanen" <petri.t.pitka...@gmail.com
> <mailto:petri.t.pitka...@gmail.com>> wrote:
>
> Unfortunately there is no proof that you principles work better
> than those form eighties. Nor there is any agreement that your
> pronciples form any improvement over the old ones. Yes you are a 
> far better player than me and shows that you are 
> - way better at reading 
> - have hugely better go understanding, principles if you like
>
> What is missing that I doubt that you can verbalise your go
> understanding to degree that by applying those principles  I could
> become substantially better player. again bulleting
> - My reading skills would not get any better hence making much of
> value any learning moot. Obviously issue on me not on your principles
> - your principles are more complex than you understand. Much of
> you know is automated to degree that it is subconsciousness
> information. Transferring that information if hard. Usually done
> by re-playing master games looking at problems i.e. training the
> darn neural net in the head
>
> If you can build Go bot about  KGS 3/4dan strength I am more than
> willing to admit you are right and would even consider buying
> your  books.
>
> Petri
>
> 2017-10-26 6:21 GMT+03:00 Robert Jasiek <jas...@snafu.de
> <mailto:jas...@snafu.de>>:
>
> On 25.10.2017 18:17, Xavier Combelle wrote:
>
> exact go theory is full of hole.
>
>
> WRT describing the whole game, yes, this is the current state.
> Solving go in a mathematical sense is a project for centuries.
>
> Actually, to my knowledge human can't apply only the exact
> go theory and
> play a decent game.
>
>
> Only for certain positions of a) late endgame, b) semeais, c) ko.
>
> If human can't do that, how it will teach a computer to do
> it magically ?
>
>
> IIRC, Martin Müller implemented CGT endgames a la Mathematical
> Go Endgames.
>
> The reason why (b) had became unpopular is because there
> is no go theory
> precise enough to implement it as an algorithm
>
>
> There is quite some theory of the 95% principle kind which
> might be implemented as approximation. E.g. "Usually, defend
> your weak important group." can be approximated by
> approximating "group", "important" (its loss is too large in a
> quick positional judgement), "weak" (can be killed in two
> successive moves), "defend" (after the move, cannot be killed
> in two successive moves), "usually" (always, unless there are
> several such groups and some must be chosen, say, randomly;
> the approximation being that the alternative strategy of large
> scale exchange is discarded).
>
> Besides, one must prioritise principles to solve conflicting
> principles by a higher order principle.
>
> IMO, such an expert system combined with tree reading and
> maybe MCTS to emulate reading used when a principle depends on
> reading can, with an effort of a few manyears of
> implementation, already achieve amateur mid dan. Not high dan
> yet because high dans can choose advanced strategies, such as
> global exchange, and there are no good enough principles for
> 

Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

Unless I mistake figure 3 shows the plot of supervised learning to
reinforcement learning, not 20 bloc/40 block

For searching mention of the 20 blocks I search for 20 in the whole
paper and did not found any other mention

than of the kifu thing.


Le 26/10/2017 à 15:10, Gian-Carlo Pascutto a écrit :
> On 26-10-17 10:55, Xavier Combelle wrote:
>> It is just wild guesses  based on reasonable arguments but without
>> evidence.
> David Silver said they used 40 layers for AlphaGo Master. That's more
> evidence than there is for the opposite argument that you are trying to
> make. The paper certainly doesn't talk about a "small" and a "big" Master.
>
> You seem to be arguing from a bunch of misreadings and
> misunderstandings. For example, Figure 3 in the paper shows the Elo plot
> for the 20 block/40 layer version, and it compares to Alpha Go Lee, not
> Alpha Go Master. The Alpha Go Master line would be above the flattening
> part of the 20 block/40 layer AlphaGo Zero. I guess you missed this when
> you say that they "only mention it to compare on kifu prediction"?
>

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[Computer-go] I present my apologizes to Robert Jasiek

[Xavier Combelle]

I present my apologizes to Robert jasiek.
To my knowledge all his behavior on this list was always correct
and my initial and my subsequent mail was inappropriate

Xavier Combelle


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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

It is just wild guesses  based on reasonable arguments but without evidence.


Le 26/10/2017 à 07:51, Hideki Kato a écrit :
> You can believe
>> Of what I understand same network architecture imply the same number of
>> block
> but David Silver told AlphaGo Master used 40 layers in 
> May. 
> http://www.bestchinanews.com/Science-Technology/10371.html
> # The paper was submitted in April.
>
> Usually, network "architecture" does not imply the number of 
> layers whereas "configulation" may do.
>
> Clearly they made 40 layers version first because it's 
> called "1st instance" where the 80 layers one is called "2nd 
> instance."  The 1st was trained 3 days and overtook AlphaGo 
> Lee.  Then they changed to the 2nd.  Awaring this fact, and 
> watching the growing curve of the 1st, I guess 40 layers was 
> not enough to reach AlphaGo Master level and so they 
> doubled the layers.
>
> Hideki
>
> Xavier Combelle: <1550c907-8b96-e4ea-1f5e-2344f394b...@gmail.com>:
>> As I understand the paper they directly created alphago zero with a 40 
>> block
>> setup.
>> They just made a reduced 20 block setup to compare on kifu prediction
>> (as far as I searched in the paper, it is the only
>> place where they mention the 20 block setup)
>> They specifically mention comparing several version of their software.
>> with various parameter
>> If the number of block was an important parameter I hope they would
>> mention it.
>> Of course they are a lot of things that they try and failed and we will
>> not know about
>> But I have hard time to believe that alphago zero with a 20 block is one
>> of them
>> About the paper, there is no mention of the number of block of master:
>> "AlphaGo Master is the program that defeated top human players by 60–0
>> in January, 2017 34 .
>> It was previously unpublished but uses the same neural network
>> architecture, reinforcement
>> learning algorithm, and MCTS algorithm as described in this paper.
>> However, it uses the
>> same handcrafted features and rollouts as AlphaGo Lee
>> and training was initialised by
>> supervised learning from human data."
>> Of what I understand same network architecture imply the same number of
>> block
>> Le 25/10/2017 à 17:58, Xavier Combelle a écrit :
>>> I understand better
>>> Le 25/10/2017 à 04:28, Hideki Kato a écrit :
>>>> Are you thinking the 1st instance could reach Master level 
>>>> if giving more training days?
>>>> I don't think so.  The performance would be stopping 
>>>> improving at 3 days.  If not, why they built the 2nd 
>>>> instance?
>>>> Best,
>>>> Hideki
>>>> Xavier Combelle: <05c04de1-59c4-8fcd-2dd1-094faabf3...@gmail.com>:
>>>>> How is it a fair comparison if there is only 3 days of training for 
>> Zero ?
>>>>> Master had longer training no ? Moreover, Zero has bootstrap problem
>>>>> because at the opposite of Master it don't learn from expert games
>>>>> which means that it is likely to be weaker with little training.
>>>>> Le 24/10/2017 à 20:20, Hideki Kato a écrit :
>>>>>> David Silver told Master used 40 layers network in May. 
>>>>>> According to new paper, Master used the same architecture 
>>>>>> as Zero.  So, Master used 20 blocks ResNet.  
>>>>>> The first instance of Zero, 20 blocks ResNet version, is 
>>>>>> weaker than Master (after 3 days training).  So, with the 
>>>>>> same layers (a fair comparison) Zero is weaker than 
>>>>>> Master.
>>>>>> Hideki
>>>>> ___
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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Xavier Combelle]

>> The reason why (b) had became unpopular is because there is no go theory
>> precise enough to implement it as an algorithm
>
> There is quite some theory of the 95% principle kind which might be
> implemented as approximation. E.g. "Usually, defend your weak
> important group." can be approximated by approximating "group",
> "important" (its loss is too large in a quick positional judgement),
> "weak" (can be killed in two successive moves), "defend" (after the
> move, cannot be killed in two successive moves), "usually" (always,
> unless there are several such groups and some must be chosen, say,
> randomly; the approximation being that the alternative strategy of
> large scale exchange is discarded).
>
> Besides, one must prioritise principles to solve conflicting
> principles by a higher order principle.
>
> IMO, such an expert system combined with tree reading and maybe MCTS
> to emulate reading used when a principle depends on reading can, with
> an effort of a few manyears of implementation, already achieve amateur
> mid dan. Not high dan yet because high dans can choose advanced
> strategies, such as global exchange, and there are no good enough
> principles for that yet, which would also consider necessary side
> conditions related to influence, aji etc. I need to work out such
> principles during the following years. Currently, the state is that
> weaker principles have identified the major topics (influence, aji
> etc.) to be considered in fights but they must be refined to create
> 95%+ principles.
>
> ***
>
> In the 80s and 90s, expert systems failed to do better than ca. 5 kyu
> because principles were only marginally better than 50%. Today, (my)
> average principles discard the weaker, 50% principles and are ca. 75%.
> Tomorrow, the 75% principles can be discarded for an average of 95%
> principles. Expert systems get their chance again! Their major
> disadvantage remains: great manpower is required for implementation.
> The advantage is semantical understanding.
>
From a software developer point of view enlighten by my knowledge of
history of ai and history of go development,
 such approximate definition is close to useless to build a software at
the current state of art.
One of the reason is as you state the considerable work it would require
to implement a huge number of imprecise rules.
As you are not a software developer, I want you to look on this comics
which state the difference between apparent difficulty and real difficulty
of developping software. https://xkcd.com/1425/
As far as I understand your task to implement such an expert system
would require the many years of implementations would be thousands of years.
As far as my experience speak the expected reward would be a win of one
or two rank and so definitely not a mid dan amateur level.

Xavier Combelle

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Re: [Computer-go] Source code (Was: Reducing network size? (Was: AlphaGo Zero))

[Xavier Combelle]

Nice to know. I wrongly believe that training such a big neural network
would need considerable hardware.

Le 25/10/2017 à 19:54, Álvaro Begué a écrit :
> There are ways to do it, but it might be messy. However, the vast
> majority of the computational effort will be in playing games to
> generate a training database, and that part is trivial to distribute.
> Testing if the new version is better than the old version is also very
> easy to distribute.
>
> Álvaro.
>
>
> On Wed, Oct 25, 2017 at 11:57 AM, Xavier Combelle
> <xavier.combe...@gmail.com <mailto:xavier.combe...@gmail.com>> wrote:
>
> Is there some way to distribute learning of a neural network ?
>
>
> Le 25/10/2017 à 05:43, Andy a écrit :
>> Gian-Carlo, I didn't realize at first that you were planning to
>> create a crowd-sourced project. I hope this project can get off
>> the ground and running!
>>
>> I'll look into installing this but I always find it hard to get
>> all the tool chain stuff going.
>>
>>
>>
>> 2017-10-24 15:02 GMT-05:00 Gian-Carlo Pascutto <g...@sjeng.org
>> <mailto:g...@sjeng.org>>:
>>
>> On 23-10-17 10:39, Darren Cook wrote:
>> >> The source of AlphaGo Zero is really of zero interest (pun
>> intended).
>> >
>> > The source code is the first-hand account of how it works,
>> whereas an
>> > academic paper is a second-hand account. So, definitely not
>> zero use.
>>
>> This should be fairly accurate:
>>
>> https://github.com/gcp/leela-zero
>> <https://github.com/gcp/leela-zero>
>>
>> --
>> GCP
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>> <http://computer-go.org/mailman/listinfo/computer-go>
>>
>>
>>
>>
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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

As I understand the paper they directly created alphago zero with a 40 block
setup.

They just made a reduced 20 block setup to compare on kifu prediction
(as far as I searched in the paper, it is the only
place where they mention the 20 block setup)

They specifically mention comparing several version of their software.
with various parameter

If the number of block was an important parameter I hope they would
mention it.

Of course they are a lot of things that they try and failed and we will
not know about

But I have hard time to believe that alphago zero with a 20 block is one
of them

About the paper, there is no mention of the number of block of master:

"AlphaGo Master is the program that defeated top human players by 60–0
in January, 2017 34 .
It was previously unpublished but uses the same neural network
architecture, reinforcement
learning algorithm, and MCTS algorithm as described in this paper.
However, it uses the
same handcrafted features and rollouts as AlphaGo Lee
and training was initialised by
supervised learning from human data."

Of what I understand same network architecture imply the same number of
block

Le 25/10/2017 à 17:58, Xavier Combelle a écrit :
> I understand better
>
>
> Le 25/10/2017 à 04:28, Hideki Kato a écrit :
>> Are you thinking the 1st instance could reach Master level 
>> if giving more training days?
>>
>> I don't think so.  The performance would be stopping 
>> improving at 3 days.  If not, why they built the 2nd 
>> instance?
>>
>> Best,
>> Hideki
>>
>> Xavier Combelle: <05c04de1-59c4-8fcd-2dd1-094faabf3...@gmail.com>:
>>> How is it a fair comparison if there is only 3 days of training for Zero ?
>>> Master had longer training no ? Moreover, Zero has bootstrap problem
>>> because at the opposite of Master it don't learn from expert games
>>> which means that it is likely to be weaker with little training.
>>> Le 24/10/2017 à 20:20, Hideki Kato a écrit :
>>>> David Silver told Master used 40 layers network in May. 
>>>> According to new paper, Master used the same architecture 
>>>> as Zero.  So, Master used 20 blocks ResNet.  
>>>> The first instance of Zero, 20 blocks ResNet version, is 
>>>> weaker than Master (after 3 days training).  So, with the 
>>>> same layers (a fair comparison) Zero is weaker than 
>>>> Master.
>>>> Hideki
>>> ___
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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Xavier Combelle]

Le 24/10/2017 à 22:41, Robert Jasiek a écrit :

> On 24.10.2017 20:19, Xavier Combelle wrote:
>> totally unrelated
>
> No, because a) software must also be evaluated and can by go theory and
What do you want evaluate the software for ? corner cases which never
have happen in a real game ?

The current testing way that deepmind used
that is: first of all making software and software-human tournament,
guess pro move, guess pro game result
was amply enough to make the best go software.

> b) software can be built on exact go theory. That currently (b) is
> unpopular does not mean unrelated.
>
It is just a wild guess. exact go theory is full of hole.
Actually, to my knowledge human can't apply only the exact go theory and
play a decent game.
If human can't do that, how it will teach a computer to do it magically ?

if you want we can setup a game were you apply only exact go theory
against me (I'm only 2 kyu)
The rules are the following, you have to apply mechanically the go
theory as a computer would do
at each move such as I could do exactly the same
and show in a detailed way how you applied it. If you won I will
recognize  the fact that the exact go
theory is not full of hole.

The reason why (b) had became unpopular is because there is no go theory
precise enough to implement it as an algorithm
and MCTS and neural network was way to use small or none part of go
theory and make a decent player.

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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

I understand better


Le 25/10/2017 à 04:28, Hideki Kato a écrit :
> Are you thinking the 1st instance could reach Master level 
> if giving more training days?
>
> I don't think so.  The performance would be stopping 
> improving at 3 days.  If not, why they built the 2nd 
> instance?
>
> Best,
> Hideki
>
> Xavier Combelle: <05c04de1-59c4-8fcd-2dd1-094faabf3...@gmail.com>:
>> How is it a fair comparison if there is only 3 days of training for Zero ?
>> Master had longer training no ? Moreover, Zero has bootstrap problem
>> because at the opposite of Master it don't learn from expert games
>> which means that it is likely to be weaker with little training.
>> Le 24/10/2017 à 20:20, Hideki Kato a écrit :
>>> David Silver told Master used 40 layers network in May. 
>>> According to new paper, Master used the same architecture 
>>> as Zero.  So, Master used 20 blocks ResNet.  
>>> The first instance of Zero, 20 blocks ResNet version, is 
>>> weaker than Master (after 3 days training).  So, with the 
>>> same layers (a fair comparison) Zero is weaker than 
>>> Master.
>>> Hideki
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Re: [Computer-go] Source code (Was: Reducing network size? (Was: AlphaGo Zero))

[Xavier Combelle]

Is there some way to distribute learning of a neural network ?


Le 25/10/2017 à 05:43, Andy a écrit :
> Gian-Carlo, I didn't realize at first that you were planning to create
> a crowd-sourced project. I hope this project can get off the ground
> and running!
>
> I'll look into installing this but I always find it hard to get all
> the tool chain stuff going.
>
>
>
> 2017-10-24 15:02 GMT-05:00 Gian-Carlo Pascutto  >:
>
> On 23-10-17 10:39, Darren Cook wrote:
> >> The source of AlphaGo Zero is really of zero interest (pun
> intended).
> >
> > The source code is the first-hand account of how it works,
> whereas an
> > academic paper is a second-hand account. So, definitely not zero
> use.
>
> This should be fairly accurate:
>
> https://github.com/gcp/leela-zero 
>
> --
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>
>
>
>
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Re: [Computer-go] Zero is weaker than Master!?

[Xavier Combelle]

How is it a fair comparison if there is only 3 days of training for Zero ?
Master had longer training no ? Moreover, Zero has bootstrap problem
because at the opposite of Master it don't learn from expert games
which means that it is likely to be weaker with little training.


Le 24/10/2017 à 20:20, Hideki Kato a écrit :
> David Silver told Master used 40 layers network in May. 
> According to new paper, Master used the same architecture 
> as Zero.  So, Master used 20 blocks ResNet.  
>
> The first instance of Zero, 20 blocks ResNet version, is 
> weaker than Master (after 3 days training).  So, with the 
> same layers (a fair comparison) Zero is weaker than 
> Master.
>
> Hideki


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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Xavier Combelle]

"In the current time, computer-go discussion and research has a very
high percentage of people discussing the side of mainly programs and
programming but I belong to the very low percentage of people
discussing mainly go-theoretical aspects of computer-go. With a higher
percentage of the latter, there would also be more discussions
resulting to something."

Now you explained what you describe what you mean by go-theoretical
aspects, which is your main area of interest,
I feel like they are totally unrelated to the purpose of this mailing list.

to quote the home page: http://computer-go.org/
"computer-go: Discussion on research and development of software that
plays the game of Go."

Now that is clear, I understand why I always felt your intervention
misplaced (because they were misplaced).

Le 24/10/2017 à 17:00, Robert Jasiek a écrit :
> On 24.10.2017 16:45, Xavier Combelle wrote:
>> I don't understand what you mean by go-theorical aspects.
>
> Go theory is an ambiguous term and means everything from informal
> ("Starting with a standard corner move can't be wrong.") via principle
> ("Usually, defend a weak important group.") to formal (
> https://senseis.xmp.net/?CycleLaw ).
>
>> and especially when applying to computer-go.
>
> Relating computer play / algorithms to go theory or vice versa adds
> another layer of difficulty indeed.
>
>> To my knowledge the only theoretical (in a
>> mathematic meaning of theoretical) approach of go is combinatorial
>> theory and it leads to very few knowledge.
>
> Other mathematical theory with practical relevance is related to
> capturing races (see Capturing Races 1 - Two Basic Groups, Thomas
> Wolf's papers etc., endgame (e.g.,
> http://home.snafu.de/jasiek/kodame.pdf and google for related proofs)
> or will be published by me later (will be quite a lot and have
> practical relevance, but you need to be patient). Research in
> mathematical go theory requires much time because exactness is often
> necessary and proving can be tricky.
>


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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Xavier Combelle]

Le 24/10/2017 à 14:35, Robert Jasiek a écrit :
> On 24.10.2017 11:45, David Ongaro wrote:
>> very seldom saw a discussion with Robert lead to anything.
>
> (You seem to only refer to discussion on this mailing list.)
>
> Apart from this being a discussion about one particular person, let me
> ignore this for a moment:
>
> In the current time, computer-go discussion and research has a very
> high percentage of people discussing the side of mainly programs and
> programming but I belong to the very low percentage of people
> discussing mainly go-theoretical aspects of computer-go. With a higher
> percentage of the latter, there would also be more discussions
> resulting to something.
>
I don't understand what you mean by go-theorical aspects. and especially
when applying to computer-go. To my knowledge the only theoretical (in a
mathematic meaning of theoretical) approach of go is combinatorial
theory and it leads to very few knowledge. Can you explain what you mean
maybe by giving example ?
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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Xavier Combelle]

Hi Robert Jasiek,

you might have a delusional way to see the game of go and life, but I
would love that you would not pollute
my mailbox with such a delusional vision. I'm certain that a lot of
person of this mailing list and other forums share my view.

To sum up, I would be pleased and I'm quite certain others too that you
consider seriously behave more like others persons.

Did you already encounter a real game with "disturbing life kos or
anti-sekis" and especially "ladders (...) beyond 250 moves" ? If not how
do you believe that Alphago would learn how to manage such situations.

Xavier Combelle

Le 23/10/2017 à 16:35, Robert Jasiek a écrit :
> On 23.10.2017 14:05, Jim O'Flaherty wrote:
>> Couldn't they be useful as part of a set of training data for newly
>> trained
>> engines and networks?
>
> All the millions of games would be very useful for many purposes.
> E.g., I want to know whether the reconstructed knowledge includes such
> basic things as terminal positions with disturbing life kos or
> anti-sekis, whether ladders are recognised beyond 250 moves etc. Not
> to mention non-go applications.
>


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Re: [Computer-go] AlphaGo Zero

[Xavier Combelle]

Le 20/10/2017 à 17:24, Robert Jasiek a écrit :
>  Why, that is easy: test! Modify ONE weight and study its effect on
> ONE aspect of human go theory, such as the occurrance (frequency) of
> independent life. No effect? Increase the modification, test a
> different weight, test a subset of adjacent weights etc. It has been
> possible to study semantics of parts of DNA, e.g., from differences
> related to illnesses. Modifications on the weights is like creating
> causes for illnesses (or improved health).
I really believe you speak of things knowing nothing about. I'm far from
an expert in neural networks but from all that I know about them :

1- It really really unlikely to work
2- It is totally unrelated with DNA
3- It was probably already tried without success
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Re: [Computer-go] AlphaGo Zero

[Xavier Combelle]

You seems to lack of knowing what is really a nano robot in current term.

They are very far to have the possibility to self replicate them self
and far more being able to dissolve the planet by doing that.

What is currently named nanorobot is simply hand assembled molecules
which have mechanical properties and need huge 

framework to be able simply move. So far to be a threat.


Le 20/10/2017 à 08:33, Robert Jasiek a écrit :
> On 20.10.2017 07:10, Petri Pitkanen wrote:
> >> 3) Where is the world-wide discussion preventing a combination of
> AI >> and (nano-)robots, which self-replicate or permanently ensure
> energy >> access, from causing extinction of mankind?
>> 3) Would it be a bad thing? All thing considered, not just human
>> point of
>> view
>
> Have you realised the potential of one successful self-duplication of
> a nano-robot? Iterate and the self-replicating nano-robots might
> dissolve the planet earth into elementary particles. Now discuss
> whether that might be good or bad. Not good for animals or plants, to
> start with.
>

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Re: [Computer-go] AlphaGo Zero

[Xavier Combelle]

They are 80 games of different version of alphago and 3 of alphago
against same version of alphago in supplementary data of
https://www.nature.com/nature/journal/v550/n7676/full/nature24270.html#supplementary-information


Le 18/10/2017 à 20:29, Richard Lorentz a écrit :
> Wow! That's very exciting. I'm glad they didn't completely shelve the
> project as they implied they might do after the match with Lee Sedol.
>
> I'm looking forward to seeing some games and "... plays unknown to
> humans", as Hassabis states.
>
> Also, I love this comment from Silver, something I have always
> promoted: The implication is that “algorithms matter much more than
> either computing or data available”.
>
> -Richard
>
>
>
> On 10/18/2017 10:50 AM, cazen...@ai.univ-paris8.fr wrote:
>> https://urldefense.proofpoint.com/v2/url?u=https-3A__deepmind.com_blog_=DwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=IPW6s_201Mkb1YsJA4v5VU1jX-PAmMmmrbwYr8hhh2w=zdcDXO2JZU2MfbTwTTrIB8JlmwOD4L11kctLG8w4ktI=
>>
>> https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nature.com_nature_index.html=DwIGaQ=Oo8bPJf7k7r_cPTz1JF7vEiFxvFRfQtp-j14fFwh71U=i0hg-cKH69CA5MsdosvezQ=IPW6s_201Mkb1YsJA4v5VU1jX-PAmMmmrbwYr8hhh2w=rb3-CGHJ_gOUzsjkKh1Ul9f7-eDNkvaWahvgs689xWA=
>>
>> Impressive!
>>
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Re: [Computer-go] How to register an account on cgos?

[Xavier Combelle]

If I remember correctly the first connection with a password is a
registration.

Le 27 sept. 2017 07:40, "James Guo via Computer-go" <
computer-go@computer-go.org> a écrit :

> Try to do some test on cgos, but failed to find the page(way) to register
> an account.
>
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Re: [Computer-go] AlphaGo and Perfect Play

[Xavier Combelle]

According to what happen in chess and according to the tee size of go game
I would say that astronomically far from perfect play is an astronomical
understatement.

Le 17 août 2017 07:17, "Cai Gengyang"  a écrit :

Does anyone here know how far AlphaGo is away from perfect play ?
Estimations ?

GengYang

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Re: [Computer-go] DeepMind's Victory over Ke Jie

[Xavier Combelle]

Le 7 juin 2017 22:21, "Cai Gengyang"  a écrit :

Hi guys,

Just a couple of questions :

1) Is it true that DeepMind's comprehensive victory over Ke Jie means that
essentially it is proven to be true that AI has definitely triumphed over
humanity ?


I would say together with the 60-0 win of the Master games yes.


2) Also, I read that AG's style is "conservative" -- i.e. it almost always
prefers the higher chance of winning by a small number of points compared
to the smaller chance of winning by a large number of points ?


Yes it is the easiest way to make a strong bot.


Thanks alot

GengYang

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Re: [Computer-go] Ke Jie vs. AlphaGo match

[Xavier Combelle]

Is it japanese tome zone?


Le 19 mai 2017 05:41, "Hiroshi Yamashita"  a écrit :

Hi,

It will be played in a week.
But there are few information about this.
Is there YouTube live available?

I found a schedule in Panda-net site.

Ke Jie vs. AlphaGo  (3 hours + 1 minute x5)
Game1May 23   11:30-18:30
Game2May 25   11:30-18:30
Game3May 27   11:30-18:30

Pair Go  May 26   09:30-12:30
Team Go  May 26   13:30-19:30

Panda net (in Japanese)
http://www.pandanet.co.jp/event/fogs/
Exploring the mysteries of Go with AlphaGo and China's top players
https://deepmind.com/blog/exploring-mysteries-alphago/

Thanks,
Hiroshi Yamashita

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Re: [Computer-go] Congratulations to Zen19X!

[Xavier Combelle]

Thanks a lot for your work on this report and all your work for computer go
in general

Le 16 mars 2017 12:55, "Nick Wedd"  a écrit :

> Congratulations to Zen19X, winner of the Spring Slow KGS bot tournament!
>
> My report is now at http://www.weddslist.com/kgs/past/S17.1/index.html
> As always, I will welcome your comments and corrections.
>
> I apologise for the very late appearance of my report. At first I put it
> off because the KGS software I use to build the crosstable had stopped
> working, and I hoped that it would be repaired (it is still broken). Then I
> was overtaken by other commitments for a few days.
>
> Nick
> --
> Nick Wedd  mapr...@gmail.com
>
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Re: [Computer-go] I can't send messages to the list any more

[Xavier Combelle]

Not with the free.fr address

Le 12 févr. 2017 11:21, "Lukas van de Wiel"  a
écrit :

> Well, Remi.
>
> Obviously, you still can, somehow.
>
> Lukas
>
> On Sun, Feb 12, 2017 at 10:18 AM, Rémi Coulom 
> wrote:
>
>> : host mail.eugeneweb.com[184.105.139.163]
>> said:
>> 554 5.7.1 : Sender address rejected: Access
>> denied (in
>> reply to RCPT TO command)
>>
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Re: [Computer-go] Will Ke Jie

[Xavier Combelle]

It is a rumor.
https://www.reddit.com/r/baduk/comments/5rtgv3/ke_jie_and_chinese_go_association_deny_recent/
There is probably négociations

Le 6 févr. 2017 14:40,  a écrit :

Is it a rumor or will ke Jie play Alpha-Go in April?

http://www.newsgd.com/news/2017-02/04/content_164638895.htm

Magnus Persson
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Re: [Computer-go] ADMIN: Lists Have Moved

[Xavier Combelle]

Strangely enougth I received all the said messages


Le 23/01/2017 à 06:20, "Ingo Althöfer" a écrit :
> ... and also not my own one from 2 minutes ago.
> I can only look it up in the archives.
>
> Strange.
>
> Ingo.
>
>
>> Gesendet: Montag, 23. Januar 2017 um 02:37 Uhr
>> Von: computer...@roveg.org
>> An: computer-go@computer-go.org, wvgc...@computer-go.org
>> Betreff: [Computer-go] ADMIN:  Lists Have Moved
>>
>>
>> Hi,
>>  The lists are running in a new place. The
>> archives and membership options should all be
>> the same.  If you have any problems, please let me
>> know.
>>  Thanks!
>>  Michael
>>
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Re: [Computer-go] Computer Go Journal

[Xavier Combelle]

No problem


Le 13/01/2017 à 13:34, Nick Wedd a écrit :
> Hi Xavier,
>
> I'm sorry, I've just sent it to someone else.
>
> Nick
>
> On 13 January 2017 at 12:18, Xavier Combelle
> <xavier.combe...@gmail.com <mailto:xavier.combe...@gmail.com>> wrote:
>
> I'm interested, What would be approx the mailing cost to send to
> France ?
>
>
> Le 13/01/2017 à 11:32, Nick Wedd a écrit :
>> I have a partial run of Computer Go", issues 3-16, dated
>> 1987-1991; also some duplicates. Is this of any interest to
>> anyone?  I live in England, and will send it to anyone willing to
>> pay mailing costs.
>>
>> Nick
>> -- 
>> Nick Wedd  mapr...@gmail.com <mailto:mapr...@gmail.com>
>>
>>
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Re: [Computer-go] Computer Go Journal

[Xavier Combelle]

I'm interested, What would be approx the mailing cost to send to France ?


Le 13/01/2017 à 11:32, Nick Wedd a écrit :
> I have a partial run of Computer Go", issues 3-16, dated 1987-1991;
> also some duplicates. Is this of any interest to anyone?  I live in
> England, and will send it to anyone willing to pay mailing costs.
>
> Nick
> -- 
> Nick Wedd  mapr...@gmail.com 
>
>
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Re: [Computer-go] Training the value network (a possibly more efficient approach)

[Xavier Combelle]

So I will start to create software, and if someone want to use it you
will be free as free software, and I already found someone

who is ready to host the server side.

From a practical point of view, I will use public key signing to
distribute go software (binary or source), so I will ask the author to
sign it and give me their public key.

Xavier Combelle


Le 12/01/2017 à 11:04, Gian-Carlo Pascutto a écrit :
> On 11-01-17 18:09, Xavier Combelle wrote:
>> Of course it means distribute at least the binary so, or the source,
>> so proprietary software could be reluctant to share it. But for free
>> software there should not any problem. If someone is interested by my
>> proposition, I would be pleased to realize it.
> It is obvious that having a 30M dataset of games between strong players
> (i.e. replicating the AlphaGo training set) would be beneficial to the
> community. It is clear that most of us are trying to do the same now,
> that is somehow trying to learn a value function from the about ~1.5M
> KGS+Tygen+GoGoD games while trying to control overfitting via various
> measures. (Aya used small network + dropout. Rn trained multiple outputs
> on a network of unknown size. I wonder why no-one tried normal L1/L2
> regularization, but then I again I didn't get that working either!)
>
> Software should also not really be a problem: Leela is free, Ray and
> Darkforest are open source. If we can use a pure DCNN player I think
> there are several more options, for example I've seen several programs
> in Python. You can resolve score disagreement by invoking GNU Go --score
> aftermath.
>
> I think it's an open question though, *how* the games should be
> generated, i.e.:
>
> * Follow AlphaGo procedure but with SL instead of RL player (you can use
> bigger or smaller networks too, many tradeoffs possible)
> * Play games with full MCTS search and small number of playouts. (More
> bias, much higher quality games).
> * The author of Aya also stated his procedure.
> * Several of those and mix :-)
>



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Re: [Computer-go] Training the value network (a possibly more efficient approach)

[Xavier Combelle]

Le 11/01/2017 à 16:14, Bo Peng a écrit :
> Hi,
>
>> How do you get the V(s) for those datasets? You play out the endgame
>> with the Monte Carlo playouts?
>>
>> I think one problem with this approach is that errors in the data for
>> V(s) directly correlate to errors in MC playouts. So a large benefit of
>> "mixing" the two (otherwise independent) evaluations is lost.
> Yes, that is a problem for Human games dataset.
>
> On the other hand, currently the SL part is relatively easier (it seems
> everyone arrives at a 50-60% accuracy), and the main challenges of the RL
> part is generating the huge number of self-play games.
>
> In self-play games we have an accurate end-game v(s) / V(s). And v(s) /
> V(s) is able to use the information in self-play games more efficiently. I
> think this can be helpful.
>
Could, some distributed workload such as fishtest for stockfish help to
generate
huge number of self-play game

If it is the case I could create the framework to use it. It is
classical programming and as such
I should be able to do it (at the opposite of Computer go software which
is hard for me by lack of practice).
Of course it means distribute at least the binary so, or the source, so
proprietary software could be reluctant to share it.
But for free software there should not any problem.

If someone is interested by my proposition, I would be pleased to
realize it.

Xavier

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Re: [Computer-go] Our Silicon Overlord

[Xavier Combelle]

It already happened
https://www.theguardian.com/technology/2016/jun/30/tesla-autopilot-death-self-driving-car-elon-musk


Le 07/01/2017 à 22:34, Nick Wedd a écrit :
> The first time someone's killed by an AI-controlled vehicle, you can
> be sure it'll be world news. That's how journalism works.
>
> Nick
>
> On 7 January 2017 at 21:24, Xavier Combelle <xavier.combe...@gmail.com
> <mailto:xavier.combe...@gmail.com>> wrote:
>
>
> > ...this is a major objective. E.g., we do not want AI driven cars
> > working right most of the time but sometimes killing people because
> > the AI faces situations (such as a local sand storm or a painting on
> > the street with a fake landscape or fake human being) outside its
> > current training and reading.
> currently I don't like to be killed by a drunk driver, and to my
> opinion
> it is very more likely to happen than an AI killing me because a
> mistake
> in programming (I know, it is not the point of view of most of people
> which want a perfect AI with zero dead and not an AI which would
> reduce
> the death on road by a factor 100)
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Re: [Computer-go] Our Silicon Overlord

[Xavier Combelle]

All the point, is that there is very little chance that you are more likely
to dead by an AI driven than a human driven as the expectation set to
AI driven is at least one order of magnitude higher than human one
before there is any hope that AI would be authorized (Actually the real
expectation is AI would be responsible of zero death)

Le 07/01/2017 à 22:35, Gonçalo Mendes Ferreira a écrit :
> Well, I don't know what is the likelihood of being hit by drunk drivers
> or AI driven cars, but if it were the same I'd prefer to have drunk
> drivers. Drunk drivers you can understand: you can improve your chances
> by making yourself more visible, do not jump from beyond obstacles, be
> more careful when crossing or not crossing before they actually stop. A
> failure in an AI car seems much more unpredictable.
>
> Gonçalo
>
> On 07/01/2017 21:24, Xavier Combelle wrote:
>>> ...this is a major objective. E.g., we do not want AI driven cars
>>> working right most of the time but sometimes killing people because
>>> the AI faces situations (such as a local sand storm or a painting on
>>> the street with a fake landscape or fake human being) outside its
>>> current training and reading. 
>> currently I don't like to be killed by a drunk driver, and to my opinion
>> it is very more likely to happen than an AI killing me because a mistake
>> in programming (I know, it is not the point of view of most of people
>> which want a perfect AI with zero dead and not an AI which would reduce
>> the death on road by a factor 100)
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Re: [Computer-go] Our Silicon Overlord

[Xavier Combelle]

> ...this is a major objective. E.g., we do not want AI driven cars
> working right most of the time but sometimes killing people because
> the AI faces situations (such as a local sand storm or a painting on
> the street with a fake landscape or fake human being) outside its
> current training and reading. 
currently I don't like to be killed by a drunk driver, and to my opinion
it is very more likely to happen than an AI killing me because a mistake
in programming (I know, it is not the point of view of most of people
which want a perfect AI with zero dead and not an AI which would reduce
the death on road by a factor 100)
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Re: [Computer-go] it's alphago

[Xavier Combelle]

To my knowledge, fishtest is also a major part of stockfish engine. It
is essential because there is lot of possible improvement and most of
them win only 2 or 3 elo points, but added, it lead to 60-70 elo points
between each release (every one year or something like that)


Le 06/01/2017 à 17:22, daniel rich a écrit :
> A closer example than the mersenne prime search is fishtest from the
> chess engine world. My understanding is that it is a key part of why
> stockfish is such a strong chessengine.
>
> https://github.com/glinscott/fishtest
>
> A large group of volunteers that essentially donate compute power to
> test changes and improve the bot. That would be a fairly cool way
> compute time to be made available to the community. The plus is that
> eventually big corporate players may lose interest to devote the same
> level of spending and compute that we have seen so far.
>
>
>
> On Fri, Jan 6, 2017 at 8:01 AM, Lukas van de Wiel
> > wrote:
>
> A project similar to the Great Mersenne Prime search might be a
> possibility to distribute the work of training the network among many
> enthousiasts, and to keep improving it by self play.
>
> On 1/6/17, Andy  > wrote:
> > What is Ray? Strongest open source bot? Anyone have a link to it?
> >
> > On Fri, Jan 6, 2017 at 3:39 AM, Hiroshi Yamashita
> > wrote:
> >
> >> If value net is the most important part for over pro level, the
> problem
> >> is
> >> making strong selfplay games.
> >>
> >> 1. make 30 million selfplay games.
> >> 2. make value net.
> >> 3. use this value net for selfplay program.
> >> 4. go to (1)
> >>
> >> I don't know when the progress will stop by this loop.
> >> But if once strong enough selfplay games are published,
> everyone can make
> >> pro level program.
> >> 30 million is big number. It needs many computers.
> >> Computer Go community may be able to share this work.
> >> I can offer Aya, it is not open-source though. Maybe
> Ray(strongest open
> >> source so far)  is better choice.
> >>
> >> Thanks,
> >> Hiroshi Yamashita
> >>
> >> - Original Message - From:  >
> >> To:  >
> >> Sent: Friday, January 06, 2017 4:50 PM
> >> Subject: Re: [Computer-go] it's alphago
> >>
> >>
> >> Competitive with Alpha-go, one developer, not possible. I do
> think it is
> >> possible to make a pro level program with one person or a small
> team.
> >> Look
> >> at Deep Zen and Aya for example. I expect I’ll get there (pro
> level) with
> >> Many Faces as well.
> >>
> >> David
> >>
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> >>
> >
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Re: [Computer-go] it's alphago

[Xavier Combelle]

I mean as a company too, until this point none has succeed


Le 05/01/2017 à 19:35, Adrian Petrescu a écrit :
> As an individual? Probably, yes.
>
> On Thu, Jan 5, 2017 at 1:34 PM, Xavier Combelle
> <xavier.combe...@gmail.com <mailto:xavier.combe...@gmail.com>> wrote:
>
>
>
> Le 05/01/2017 à 02:16, Yamato a écrit :
> > Yes, it is AlphaGo. I am relieved that DeepMind clarified this.
> >
> > Honestly I got a little frustrated that many people didn't think
> that
> > was AlphaGo. It was almost clear to me because I know the
> difficulty of
> > developing AlphaGo-like bots.
> thanks for this insight, if I understand well developing a bot
> competitive with alphago
> is nearly an impossible task?
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Re: [Computer-go] it's alphago

[Xavier Combelle]

Le 05/01/2017 à 02:16, Yamato a écrit :
> Yes, it is AlphaGo. I am relieved that DeepMind clarified this.
>
> Honestly I got a little frustrated that many people didn't think that
> was AlphaGo. It was almost clear to me because I know the difficulty of
> developing AlphaGo-like bots.
thanks for this insight, if I understand well developing a bot
competitive with alphago
is nearly an impossible task?
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Re: [Computer-go] Our Silicon Overlord

[Xavier Combelle]

Le 05/01/2017 à 07:37, Robert Jasiek a écrit :
> On 04.01.2017 22:08, "Ingo Althöfer" wrote:
>> humanity's last hope
>
> The "last hope" are theoreticians creating arcane positions far
> outside the NN of AlphaGo so that its deep reading would be
> insufficient compensation! Another chance is long-term, subtle
> creation and use of aji.
>
The problem is that you have to find a way to constraint alphago to
reach the position you have prepared it will be very hard because it has
the choice of half of the moves which leads to the position.

From computer science point of view, theoricaly, the best move on an
arbitrary position being a PSPACE hard problem, any problem at least
easier than PSPACE could translate in a go problem. So there is a huge
amount of difficult problems (understand impossible to solve except on
toy size) which could be setup as target go positions but the real
problems is that you have to reach this position which are very unlikely
to happen in a real game.

An easy way to win against Alphago strength level of bot is to make two 
deterministic version of it, make it play one against the other and
replay the moves of the wining side.

--
Xavier

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Re: [Computer-go] NATURE: Top 10 people 2016

[Xavier Combelle]

It's pretty ironic that they put in the list the funder of scihub
and that in the same time have a firm policy of paywalling everything
for example by putting
pressure on deepmind to drawback
their preprint from alphago website.

I suppose that they did have really no other choice.

Le 19/12/2016 à 19:46, "Ingo Althöfer" a écrit :
> Hi,
> NATURE published a list of top 10 people in science 2016:
> http://www.nature.com/news/nature-s-10-1.21157
>
> Demis Hassabis, manager of the AlphaGo team, is in the list.
>
> Congratulations!
>
> Ingo.
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Re: [Computer-go] Deep Zen vs Cho Chikun -- Round 3

[Xavier Combelle]

thanks a lot


Le 24/11/2016 à 12:31, Hideki Kato a écrit :
> Ah, yes.  Maybe he didn't push "resign".
>
> Hideki
>
> Xavier Combelle: <1d25b061-1f25-497e-c270-ee2040602...@gmail.com>:
>> Hi Hideki
>> Sorry in kgs, the game has no result in kgs, did it ended with pro
>> resignation ?
>> Le 23/11/2016 à 23:03, Hideki Kato a écrit :
>>> Thanks David.
>>> It's now.
>>> In the same afternoon, Zen vs Yonil Ha 6p was played on KGS as a part 
>>> of Neyagawa Igo Shogi Festival in Neyagawa city, Osaka, Japan.  
>>> (Zen19X vs neyagawa. The time was set 4 hours to avoid KGS's time 
>>> control and actually a move was played in 30s).
>>> This Zen ran on a dual Xeon server with one nVidia GTX-1080 in my 
>>> room.  I ran seven threads.  This shows recent Zen on a PC with a 
>>> highend GPU is enough to beat pro at short-time settings.  
>>> Also, Zen on a dual-core laptop (ThinkPad X250; Core i7 5600U@2.6 GHz) 
>>> beat a pro a few times in personal trials (also fast games).
>>> Hideki
>>> David Fotland: <06c901d245a8$d41405f0$7c3c11d0$@smart-games.com>:
>>>> Congratulations to Zen for playing so well against a strong pro. It 
>> won't 
>>>> be long until anyone can get a pro strength go program that runs on 
>> their 
>>>> ordinary PC.
>>>> David
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Re: [Computer-go] Deep Zen vs Cho Chikun -- Round 3

[Xavier Combelle]

Hi Hideki

Sorry in kgs, the game has no result in kgs, did it ended with pro
resignation ?


Le 23/11/2016 à 23:03, Hideki Kato a écrit :
> Thanks David.
>
> It's now.
>
> In the same afternoon, Zen vs Yonil Ha 6p was played on KGS as a part 
> of Neyagawa Igo Shogi Festival in Neyagawa city, Osaka, Japan.  
> (Zen19X vs neyagawa. The time was set 4 hours to avoid KGS's time 
> control and actually a move was played in 30s).
>
> This Zen ran on a dual Xeon server with one nVidia GTX-1080 in my 
> room.  I ran seven threads.  This shows recent Zen on a PC with a 
> highend GPU is enough to beat pro at short-time settings.  
>
> Also, Zen on a dual-core laptop (ThinkPad X250; Core i7 5600U@2.6 GHz) 
> beat a pro a few times in personal trials (also fast games).
>
> Hideki
>
> David Fotland: <06c901d245a8$d41405f0$7c3c11d0$@smart-games.com>:
>> Congratulations to Zen for playing so well against a strong pro. It won't 
>> be long until anyone can get a pro strength go program that runs on their 
>> ordinary PC.
>>
>> David

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Re: [Computer-go] do you know King of Tsumego: Panda Sensei on android

[Xavier Combelle]

Le 26/10/2016 à 20:41, David Ongaro a écrit :
>
>> On Oct 26, 2016, at 11:07 AM, Xavier Combelle
>> <xavier.combe...@gmail.com <mailto:xavier.combe...@gmail.com>> wrote:
>>
>> Le 26/10/2016 à 19:42, David Ongaro a écrit :
>>
>>>
>>>> On Oct 26, 2016, at 9:32 AM, Xavier Combelle
>>>> <xavier.combe...@gmail.com <mailto:xavier.combe...@gmail.com>> wrote:
>>>>
>>>> but it seems to me there is a problem in this variation proposed by
>>>> Haylee:
>>>> http://eidogo.com/#3z7gfMaeI:0,7 <http://eidogo.com/#3z7gfMaeI:0,7>
>>>
>>> You’re right. Instead of q16 B should play t14 immediately.
>>> (Interestingly: if there would be a white stone on t13 it would be a
>>> ‘eternal life' situation (because of the t14, t17, t15, t16, t14
>>> cycle).)
>>>
>> Actually if I don't mistake q16 at t14 don't work it, it was seen in
>> the video and lead to a seki http://eidogo.com/#aA8PgSSk:0,1,0
>
> Without the q19, r18 exchange it would be seki, but after this
> exchange its a roku moku nakade (http://senseis.xmp.net/?RabbittySix}.
> I.e. if W answers t14 at q16, B can play t17, t15, r17, t19, s17 (for
> proving purposes, no need to actually play it unless B has to take
> white out during the game.)
>
>
yes it is rabbity six here the correct sequence (unless I do mistake)
http://eidogo.com/#x4qoDsY6
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Re: [Computer-go] do you know King of Tsumego: Panda Sensei on android

[Xavier Combelle]

Le 26/10/2016 à 19:42, David Ongaro a écrit :

>
>> On Oct 26, 2016, at 9:32 AM, Xavier Combelle
>> <xavier.combe...@gmail.com <mailto:xavier.combe...@gmail.com>> wrote:
>>
>> but it seems to me there is a problem in this variation proposed by
>> Haylee:
>> http://eidogo.com/#3z7gfMaeI:0,7 <http://eidogo.com/#3z7gfMaeI:0,7>
>
> You’re right. Instead of q16 B should play t14 immediately.
> (Interestingly: if there would be a white stone on t13 it would be a
> ‘eternal life' situation (because of the t14, t17, t15, t16, t14 cycle).)
>
Actually if I don't mistake q16 at t14 don't work it, it was seen in the
video and lead to a seki http://eidogo.com/#aA8PgSSk:0,1,0

For the full info I didn't copy the full tsumego but only from a
particular point of the video.

Here is the sequence from the start http://eidogo.com/#zHS966X5 (unless
I do mistake)

If anyone is interested and don't want to watch the full video, I can
copy all variations for this tsumego



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[Computer-go] do you know King of Tsumego: Panda Sensei on android

[Xavier Combelle]

Hi !
Do you know King of Tsumego: Panda Sensei on android

https://play.google.com/store/apps/details?id=pandanet.tsumego=fr

It seem that he won against the best go players during the pair go
championship

http://www.usgo.org/news/2016/07/panda-sensei-tsume-go-challenge-kicks-off-pair-go-world-cup-in-tokyo/

The official page of the event where one can find the problems of the
challenge between man and machine
http://www.pandanet.co.jp/event/PandaSensei/index_e.htm

If you want to see the problem solutions proposed by Haylee, you can
watch Haylee video on
https://www.youtube.com/watch?v=aAqXq4pFtNs(It's where I learn about the
competition and the app)

but it seems to me there is a problem in this variation proposed by Haylee:
http://eidogo.com/#3z7gfMaeI:0,7 
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Re: [Computer-go] Congratulations to Zen!

[Xavier Combelle]

thanks a lot, it was more a surprise than anything else. What are the
difference of rules between open and close division ?

2016-08-09 9:23 GMT+02:00 Nick Wedd <mapr...@gmail.com>:

> I could create a cross-table for the Open division. If there are enough
> players in future Open divisions, I will. But, with only two players, and
> almost all games decided by one of the players running out of time or by
> the other failing to appear at all, I didn't bother.
>
> Maybe I'll do it anyway. It won't be difficult. But at present, all the
> game results are shown in what looks like Czech, and my crosstable-building
> script won't understand them.
>
> Nick
>
> On 8 August 2016 at 23:48, Xavier Combelle <xavier.combe...@gmail.com>
> wrote:
>
>> Is it volunteer that there is no cross table for open division ? If I
>> understand well there is only two bot on it.
>>
>> 2016-08-08 13:59 GMT+02:00 Nick Wedd <mapr...@gmail.com>:
>>
>>> Congratulations to Zen19X, winner of yesterdays's KGS bot tournament!
>>>
>>> My report, at http://www.weddslist.com/kgs/past/125/index.html , is
>>> longer than usual. I hope you will email me with your comments and
>>> corrections.
>>>
>>> Nick
>>> --
>>> Nick Wedd  mapr...@gmail.com
>>>
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Re: [Computer-go] Congratulations to Zen!

[Xavier Combelle]

Is it volunteer that there is no cross table for open division ? If I
understand well there is only two bot on it.

2016-08-08 13:59 GMT+02:00 Nick Wedd :

> Congratulations to Zen19X, winner of yesterdays's KGS bot tournament!
>
> My report, at http://www.weddslist.com/kgs/past/125/index.html , is
> longer than usual. I hope you will email me with your comments and
> corrections.
>
> Nick
> --
> Nick Wedd  mapr...@gmail.com
>
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Re: [Computer-go] KGS 8-dan and strong opponents

[Xavier Combelle]

I must say Zen19A has a very nice play style

2016-06-21 10:38 GMT+02:00 Hideki Kato :

> Dear Ingo and others,
>
> I'd like to note that Zen19A limits the handicap upto 4 stones
> instead of usual 6.  This is the reason the account is not Zen19
> but Zen19A.  #It's weill known that MC bots are not good at
> (high) handicap games.
>
> Best, Hideki
>
> Ingo Althofer:
>  >:
> >Hi,
> >
> >for the first time there is a bot with "stable" 8-dan rank on KGS:
> >Zen19A, an alternative Zen account.
> >
> >http://www.gokgs.com/gameArchives.jsp?user=zen19a
> >
> >Congratulations to the Zen team!
> >
> >And an observation:
> >Already several games against human players with 8-dan
> >or 9-dan rank have been played by Zen19A in the last
> >12 hours.
> >
> >Likely, opponents in this strong range will help the
> >programmers to improve their brainchild further.
> >
> >Ingo.
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Re: [Computer-go] DarkForest is open-source now.

[Xavier Combelle]

for me it's clearly GPL violation

2016-06-10 22:17 GMT+02:00 Darren Cook :

> >> At 5d KGS, is this the world's strongest MIT/BSD licensed program? ...
> >> actually, is there any other MIT/BSD go program out there? (I thought
> >> Pachi was, but it is GPLv2)
> >
> > Huh, that's interesting, because Darkforest seems to have copy-pasted
> > the pachi playout policy:
> >
> >
> https://github.com/facebookresearch/darkforestGo/blob/master/board/pattern.c#L36
> >
> > https://github.com/pasky/pachi/blob/master/playout/moggy.c#L101
>
> Uh-oh. Though it does say "inspired by" at the top, and also that it is
> not used by the main engine:
>
> // This file is inspired by Pachi's engine
> //   (https://github.com/pasky/pachi).
> // The main DarkForest engine (when specified
> //   with `--playout_policy v2`) does not depend on it.
> // However, the simple policy opened with
> //   `--playout_policy simple` will use this library.
>
>
> Darren
>
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Re: [Computer-go] McMahon tournaments on KGS

[Xavier Combelle]

AFter watching that it was a 13x13 tournament I would say that the more
probable is that KGS don't know how to handle handicap tournament with
small board

2016-05-18 18:30 GMT+02:00 Xavier Combelle <xavier.combe...@gmail.com>:

> According to https://www.gokgs.com/help/_fr_FR/tournMcMahon.html McMahon
> avoid high handicap. You could also setup a swiss  with handicap tournament
>
> 2016-05-18 18:06 GMT+02:00 Nick Wedd <mapr...@gmail.com>:
>
>> I had been persuaded, by postings to this list, to run the monthly KGS
>> bot tournaments with a McMahon division.
>>
>> I now think that this probably won't work.  I have run a test McMahon
>> tournament. No handicaps were assigned, or reported in the results.  This
>> *may* have been because none of the entrants had a confirmed rating (one
>> had a ? rating, four were unrated), but I doubt it.  Even if all the
>> players were treated as having the same initial rating (probably 8k, where
>> I had set the lower bar), handicaps should have been used by the final
>> rounds.
>>
>> You can see the results at
>> http://www.gokgs.com/tournEntrants.jsp?sort=n=1040
>>
>> I will be willing to run another test tournament, today or tomorrow, if
>> someone can provide a "Ranked Robot" to help with the test.  It should be
>> able to beat gnugo3pt8 consistently, and should preferably be willing to
>> play 9x9 Go.  All you need to do is leave it running on KGS in tournament
>> mode, and email me with its name.  I have left gnugo3pt8 running, to
>> restore its confirmed rating.
>>
>> Nick
>> --
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>>
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Re: [Computer-go] McMahon tournaments on KGS

[Xavier Combelle]

According to https://www.gokgs.com/help/_fr_FR/tournMcMahon.html McMahon
avoid high handicap. You could also setup a swiss  with handicap tournament

2016-05-18 18:06 GMT+02:00 Nick Wedd :

> I had been persuaded, by postings to this list, to run the monthly KGS bot
> tournaments with a McMahon division.
>
> I now think that this probably won't work.  I have run a test McMahon
> tournament. No handicaps were assigned, or reported in the results.  This
> *may* have been because none of the entrants had a confirmed rating (one
> had a ? rating, four were unrated), but I doubt it.  Even if all the
> players were treated as having the same initial rating (probably 8k, where
> I had set the lower bar), handicaps should have been used by the final
> rounds.
>
> You can see the results at
> http://www.gokgs.com/tournEntrants.jsp?sort=n=1040
>
> I will be willing to run another test tournament, today or tomorrow, if
> someone can provide a "Ranked Robot" to help with the test.  It should be
> able to beat gnugo3pt8 consistently, and should preferably be willing to
> play 9x9 Go.  All you need to do is leave it running on KGS in tournament
> mode, and email me with its name.  I have left gnugo3pt8 running, to
> restore its confirmed rating.
>
> Nick
> --
> Nick Wedd  mapr...@gmail.com
>
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Re: [Computer-go] Hajin Lee will play a live commented game against CrazyStone

[Xavier Combelle]

That's fantastic

I suppose crazystone will play with crazystone account, but what will be
her handle ?

2016-05-16 9:50 GMT+02:00 Rémi Coulom :

> Hi,
>
> I am very happy to announce that Hajin Lee will play a live commented game
> against Crazy Stone on Sunday, at 8PM Korean time. The game will take place
> on KGS, and she will make live comments on her youtube channel.
>
> Haylee's youtube:
> https://www.youtube.com/c/HayleesWorldofGoBaduk
>
> Rémi
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[Computer-go] deep mind has moved to tensorflow. Wonder if alphago has moved.

[Xavier Combelle]

Reed on google blog that deepmind is moving to tensorflow. I really would
like to know if alphago has migrated too. I know that Aja Huang read this
mailing list, so maybe he has an answer.
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Re: [Computer-go] BetaGo?

[Xavier Combelle]

I can confirm that github project and kgs bot are different according to
the github project owner see
https://www.reddit.com/r/baduk/comments/4ew30e/betago_create_your_own_go_bot_using_deep_neural/d2471hf

2016-04-19 17:11 GMT+02:00 Rémi Coulom :

> Anybody knows who is the author of BetaGo? It is playing with account
> GoBeta on KGS, and is 6d.
>
> I found this project:
> http://maxpumperla.github.io/betago/
>
> But it seems weak.
>
> Rémi
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Re: [Computer-go] computergo.org

[Xavier Combelle]

2016-03-17 16:16 GMT+01:00 Joshua Shriver :

> Does anyone have interest in that domain name? I'd be willing to
> transfer it to a new owner for free.  It came up a year or so back and
> I grabbed it just in case but never used it.
>
> Rather see it go to someone who can use it rather than squat. It's
> already for another year.
>
> -Josh
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I would vote for redirecting to computer-go.org if everybody agree (or the
other direction should be good too)
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Re: [Computer-go] AlphaGo won first game!

[Xavier Combelle]

This comment should be very good, it was done by a 9 dan pro, the top rank
in go.



2016-03-09 9:27 GMT+01:00 Sergey Nikolenko :

> Everybody here probably knows it, but just in case -- there's a
> commented broadcast uploaded here:
> http://www.youtube.com/watch?v=vFr3K2DORc8
> I don't play well enough to understand how good the commentary is, though.
>
> With best regards,
> Sergey Nikolenko.
>
>
> On Wed, Mar 9, 2016 at 10:55 AM, Marc Landgraf 
> wrote:
> > It was pointed out by Lee Sedol after the game and Kim Myungwan during
> > the game, that Q5 should have been better at R4. I would say this was
> > the final stage of the middle game. The result from the game left Lee
> > Sedol with an unwinnable endgame. And "by resignation" is meaningless
> > here. It is just a matter of personal preference if pros resign heir
> > close games, even if their are lost by 0.5 or if they decide to
> > resign. In this game most counts had AlphaGo 3-6 points ahead.
> >
> > 2016-03-09 8:49 GMT+01:00 Jim O'Flaherty :
> >> Congratulations, AlphaGo and team. And by resignation! That's fantastic!
> >>
> >> Anyone know where the tipping point was? Did Sedol get the end game
> order
> >> just slightly off and AlphaGo took advantage? Or was their an earlier
> poor
> >> move by Sedol and/or surprising (and good) move by AlphaGo? I'm WAY too
> weak
> >> a player to even make stupid guesses. Any links to in depth analysis
> would
> >> be greatly appreciated!
> >>
> >> On Wed, Mar 9, 2016 at 1:46 AM, René van de Veerdonk
> >>  wrote:
> >>>
> >>> wow .. congrats to the AlphaGo team!!
> >>>
> >>> On Tue, Mar 8, 2016 at 11:43 PM, Hiroshi Yamashita 
> >>> wrote:
> 
>  AlphaGo won 1st game against Lee Sedol!
> 
>  Hiroshi Yamashita
> 
>  ___
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> >>>
> >>>
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> >>
> >>
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Re: [Computer-go] AlphaGo won first game!

[Xavier Combelle]

Congrats to Aja and alphago team

2016-03-09 8:43 GMT+01:00 Hiroshi Yamashita :

> AlphaGo won 1st game against Lee Sedol!
>
> Hiroshi Yamashita
>
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Re: [Computer-go] Mastering the Game of Go with Deep Neural Networks and Tree Search

[Xavier Combelle]

2016-02-01 12:24 GMT+01:00 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...).
>

I just want to point that according to Myungwan Kim 9p (video referenced in
this thread) on the first game, Alpha Go did some mistake early in the game
and was behind during nearly the whole game so some of his moves should be
weak in game-theoric point of view.
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[Computer-go] A proposition to improve neural network based on min max

[Xavier Combelle]

I had got an idea but I don't think I'm strong enough programmer to
implement it. (In particular I know quite nothing about neural network)
So I submit it here.

If we have a neural network which is able to evaluate all the positions of
a board.
The following might help to improve it.

>From a position given:
check the max value of all the evaluations
go to the next level in the tree
check the min value of all the evaluations
if the min value < max value train the network at the root level to target
max value for the original position
else go to next level and continue

The reason why I think it could help is because the evaluation at a deeper
level
should be easier and so better than at a less deep level.

Please give all your feedback on this idea (even: it's a stupid idea/you
should implement it are welcome)
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Re: [Computer-go] Game Over

[Xavier Combelle]

here a comment by Antti Törmänen
http://gooften.net/2016/01/28/the-future-is-here-a-professional-level-go-ai/

2016-01-28 11:19 GMT+01:00 Darren Cook :

> > If you want to view them in the browser, I've also put them on my blog:
> >
> http://www.furidamu.org/blog/2016/01/26/mastering-the-game-of-go-with-deep-neural-networks-and-tree-search/
> > (scroll down)
>
> Thanks. Has anyone (strong) made commented versions yet? I played
> through the first game, but it just looks like a game between two
> players much stronger than me :-)
>
> (Ingo, are you analyzing them with e.g. CrazyStone? Is there a
> particular point where it adjusts who it thinks is winning?)
>
> Darren
>
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Re: [Computer-go] PhD thesis on MCTS

[Xavier Combelle]

Thanks, it was nice.

I have a question did you tried to implement Nested montecarlo tree search
in two player game ?

If I remembered well something like this was envisioned in this mailing
list.

2015-12-30 13:29 GMT+01:00 Hendrik Baier :

> Hello list,
>
> I recently defended my PhD. Maybe it's of interest to some of you who
> are applying MCTS to one-player or adversarial two-player domains.
> You can download a revised edition (basically fewer typos than the
> printed thing) here:
> http://hendrikbaier.jimdo.com/research/
>
> Cheers,
> Hendrik
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Re: [Computer-go] How to only play games with correct handicap on KGS?

[Xavier Combelle]

If you refuse the handicap, the games will be *without* handicap not *with
correct* handicap

2015-10-01 19:13 GMT+02:00 Gonçalo Mendes Ferreira :

> I'm not sure but try removing the free handicap placement commands from
> the list of supported commands, returned by the command list_commands. The
> commands should be set_free_handicap and place_free_handicap.
>
> The documentation on kgsGTP reads "If your engine does not support these
> commands then handicap games will be refused.".
>
> Gonçalo F.
>
>
> On 01/10/2015 16:58, Martin Mueller wrote:
>
>> I have a new version of Fuego playing on KGS as ranked robot fuego19. I
>> would like people to play with correct handicap to get a more reliable
>> rating. Is there a way to do this with kgsgtp?
>>
>> Thanks
>>
>> Martin
>>
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Re: [Computer-go] Neural Network Chess Computer Abandons Brute Force For "Human" Approach

[Xavier Combelle]

I found interesting the concept of Probability limited search

2015-09-15 2:58 GMT+02:00 Ray Tayek :

>
> http://games.slashdot.org/story/15/09/14/219/neural-network-chess-computer-abandons-brute-force-for-human-approach
>
> Posted by samzenpus  on Monday September 14, 2015
> @06:53PM from the how-about-a-nice-game-of-global-thermonuclear-war dept.
>
> An anonymous reader writes: *A new chess AI utilizes a neural network to
> approach the millions of possible moves in the game without just throwing
> compute cycles  at the problem the way
> that most chess engines have done since Von Neumann. 'Giraffe' returns to
> the practical problems which defeated chess researchers who tried to create
> less 'systematic' opponents in the mid-1990s, and came up against the
> (still present) issues of latency and branch resolution in search. Invented
> by an MSc student at Imperial College London, Giraffe taught itself chess
> and reached FIDE International Master level
> 
> on a modern mainstream PC within three days.*
>
> --
> Honesty is a very expensive gift. So, don't expect it from cheap people - 
> Warren Buffetthttp://tayek.com/
>
>
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Re: [Computer-go] Congratulations to Zen!

[Xavier Combelle]

the link is wrong but the label is correct it is indeed
http://www.weddslist.com/kgs/past/115/index.html
http://www.weddslist.com/kgs/past/114/index.html

2015-08-10 19:29 GMT+02:00 Nick Wedd mapr...@gmail.com:

 Congratulations to Zen19X,  winner of yesterday's 13x13 KGS bot
 tournament, with 17 wins from 18 games!

 My report is at http://www.weddslist.com/kgs/past/115/index.html
 http://www.weddslist.com/kgs/past/114/index.html
 As usual I welcome your comments and corrections.

 Nick
 --
 Nick Wedd  mapr...@gmail.com

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Re: [Computer-go] EGC2015 Events

[Xavier Combelle]

Just curious, why in the statistics it is mentioned  1475 players and in
the list only 602. Does the list mention only players having playing
recently ?


2015-07-29 21:32 GMT+02:00 Rémi Coulom remi.cou...@free.fr:

 Lee Hajin is also quite a bit weaker than Yoda Norimoto or Cho Chikun.

 BTW, this gives me the opportunity to advertise my new web site that rates
 go professionals with the WHR rating algorithm and go4go.net data:

 http://www.goratings.org/

 Rank/Name/Elo

 108 Yoda Norimoto 2274
 183 Cho Chikun 2188
 448 Lee Hajin 1957

 Rémi


 On 07/29/2015 09:22 PM, Petr Baudis wrote:

Indeed.  We (well, mainly I) thought that since Aya is running on
 a weaker computer, 5 stones might be about right, but now I'm a bit
 worried that I made the game too tough for white after all.

Still, there's a big audience (surprised us a bit), maybe 150 people,
 and they seem to be enjoying it!

 On Wed, Jul 29, 2015 at 09:14:14PM +0200, Rémi Coulom wrote:

 Great! Thanks. 5 stones against Aya is brave.

 On 07/29/2015 08:21 PM, Petr Baudis wrote:

Hi!

There are several Computer Go events on EGC2015.  There was a small
 tournament of programs, played out on identical hardware by each,
 won by Aya:

 https://www.gokgs.com/tournEntrants.jsp?sort=sid=981

Then, one of the games, Aya vs. Many Faces, was reviewed by Lukas
 Podpera 6d:

 https://www.youtube.com/watch?v=_3Lk1qVoiYM

Right now, Hajin Lee 3p (known for her live commentaries on Youtube
 as Haylee) is playing Aya (giving 5 stones) and commenting live:

 https://www.youtube.com/watch?v=Ka2ilmu7Eo4

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Re: [Computer-go] Hex is solved ?

[Xavier Combelle]

I allowed my self to remove the link from the wikipedia page as non pair
reviewed

2015-07-29 15:53 GMT+02:00 Ingo Althöfer 3-hirn-ver...@gmx.de:

 Hi Rèmi,

 gorget it - no serious work.

 Ingo.


  Gesendet: Dienstag, 28. Juli 2015 um 15:38 Uhr
  Von: Rémi Coulom remi.cou...@free.fr
  An: computer-go@computer-go.org
  Betreff: [Computer-go] Hex is solved ?
 
  Hi,
 
  I have just been told by a colleague that Edouard Rodrigues solved hex
 mathematically. I was very surprised because I had never heard about it.
 
  The web site with the proof and optimal strategy is there:
  http://jeudhex.com/?page_id=17
 
  I did not look at it in details, but it seems his method can find an
 optimal move on any position and any board size.
 
  Did the computer-hex people of this list knew about it? I know there was
 an Hex tournament in Leiden, so I suppose the computer Hex community might
 not be aware of this result. Or maybe the mathematical result is wrong?
 
  Please circulate this information to the Hex specialists. I am curious
 about their opinion.
 
  I'll take time tomorrow to study that web site a little.
 
  Rémi
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Re: [Computer-go] CGT in Clobber ?!

[Xavier Combelle]

I wonder what was the algorithm for your first bot. Alpha Beta ?

2015-07-14 9:45 GMT+02:00 Ingo Althöfer 3-hirn-ver...@gmx.de:

 Hello Josef, hello all,

 Josef Moudrik j.moud...@gmail.com
  ... As far as I know, combinatorial game theory is not used
  in modern Go engines, despite its nice theoretical properties.

 Let me tell you an anecdote from CGT history: In Februar 2002, there
 was a week-long conference on Algorithmic Combinatorial Game Theory
 in Schloss Dagstuhl (Germany). It was years before the Monte Carlo
 revolution, and in those days many hopes for strong go bots were
 on CGT.

 In the beginning of the week, the simple board game Clobber
 (perfectly suited for use of CGT) was introduced - and a human
 Clobber tournament was planned for the evenings.
 I proposed to write a Clobber bot (without using CGT), and
 two other participants followed me. Within hours, our bots
 were ready. And they crushed the humans (on 6x5 board size)
 terribly. So, we were excluded from the evening tournament
 (and had to play our own bot competition).  The only comment on
 my bot by senior Elwyn Berlekamp (one of the fathers of modern
 CGT; co-author of Winning ways) was: Why does it have such an
 ugly graphical interface?  It seemed, Prof. Berlekamp was dis-
 appointed by the fact that Clobber bots were so strong without
 using any CGT.

 Photo with (almost) the whole group of 2002-participants:
 https://www.dagstuhl.de/Gruppenbilder/02081.A.B.JPG
 In the very first row (sitting): third person from left
 is John Tromp (the man from the Tromp-Taylor rules, from
 the Tromp-Cook bet, from go position counting and and and).
 In the same row the man with the diagonal go board is
 Martin Mueller. Directly behind Martin stands Richard Nowakowski
 (with red shirt), co-inventor of Clobber (in Summ,er 2001, together
 with M.H. Albert and J.P. Grossman).

 https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=02081

 Looking forward to meet you in Liberec!
 Ingo.
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Re: [Computer-go] gtp question for topological go program

[Xavier Combelle]

It's not clear why you would want that your engine have control on
communication. Can you explain your use case ?

2015-06-08 7:28 GMT+02:00 Ray Tayek rta...@ca.rr.com:

 hi, still implementing gtp for my topological go program. it sorta works
 with gogui.

 looking at the gnugo implementation, it seems all passive in that it
 simply reacts to commands.

 so it looks like one side (or some other controller) has to be the passive
 side and react to the commands (like gnugo), and one side need to be active
 to do the setup and issue genmove commands (like gogui).

 currently my program waits for the player to move before sending it's
 repsonse (the move) back. looks like gogui uses something called:
 runLengthyCommand() for this. and some complicated code.

 is there any documentation on how to be the active side of gtp?

 is it possible for both sides to issue commands?

 any pointers will be appreciated.

 thanks

 --
 Honesty is a very expensive gift. So, don't expect it from cheap people -
 Warren Buffett
 http://tayek.com/

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Re: [Computer-go] Learning from CGOS

[Xavier Combelle]

In which langage will it be ?

2015-03-28 23:58 GMT+01:00 Joshua Shriver jshri...@gmail.com:

 What elements did you like about CGOS and what do you wish for?

 I've begun writing a new version from scratch that isn't TCL based.
 With the aim for future use and also open source and open to public
 commits.

 -Josh
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Re: [Computer-go] [ANN] Michi - 15x15 ~6k KGS in 540 lines of Python code

[Xavier Combelle]

very nice job

2015-03-25 16:36 GMT+01:00 Petr Baudis pa...@ucw.cz:

   Hi!

   So what's the strongest program you can make with minimum effort
 and code size while keeping maximum clarity?  Chess programers
 were exploring this for long time, e.g. with Sunfish, and that inspired
 me to try out something similar in Go over a few evening recently:

 https://github.com/pasky/michi

 Unfortunately, Chess rules are perhaps more complicated for humans,
 but much easier to play for computers!  So the code is longer and more
 complicated than Sunfish, but hopefully it is still possible to
 understand it for a Computer Go newbie over a few hours.  I will welcome
 any feedback and/or pull requests.

   Contrary to other minimalistic UCT Go players, I wanted to create
 a program that actually plays reasonably.  It can beat many beginners
 and on 15x15 fares about even with GNUGo; even on 19x19, it can win
 about 20% of its games with GNUGo on a beefier machine.  Based on my
 observations, the limiting factor is time - Python is slw and
 a faster language with the exact same algorithm should be able to speed
 this up at least 5x, which should mean at least two ranks level-up.
 I attempt to leave the code also as my legacy, not sure if I'll ever
 get back to Pachi - these parts of a Computer Go program I consider most
 essential.  The biggest code omission wrt. strength is probably lack of
 2-liberty semeai reading and more sophisticated self-atari detection.


   P.S.: 6k KGS estimate has been based on playtesting against GNUGo over
 40-60 games - winrate is about 50% with 4000 playouts/move.  Best I can
 do...  But you can connect the program itself to KGS too:

 http://www.gokgs.com/gameArchives.jsp?user=michibot

 --
 Petr Baudis
 If you do not work on an important problem, it's unlikely
 you'll do important work.  -- R. Hamming
 http://www.cs.virginia.edu/~robins/YouAndYourResearch.html
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Re: [Computer-go] Go AI

[Xavier Combelle]

Thanks a lot looks like a good overview of go and computer go
(I only read the transcript because the video don't work on my noflash
system)

2015-02-25 0:28 GMT+01:00 Michael Alford m...@aracnet.com:

 Apology, forgot the link:

 http://blog.fogcreek.com/go-and-artificial-intelligence-tech-talk/


 On 2/24/15 3:26 PM, Michael Alford wrote:

 This link appeared in today's AGA E-journal. It mentions MoGo, Zen, and
 Crazy Stone.

 Michael

 ---

 http://en.wikipedia.org/wiki/Pale_Blue_Dot



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