Re: [Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

[Marc Landgraf]

There is not much to achieve there though.

It is expected that an AI will be able to outplay a Human opponent simply
on micro tricks. Perfect single unit micromanagment across the entire map
can easily gain a large enough edge, that the strategic decision making
with imperfect information doesn't have to reach too high levels.
So most likely any win the AI will achieve will be discredited by the Human
players due to this.

2017-12-19 16:26 GMT+01:00 Andy :

> Google has already announced their next step -- Starcraft2. But so far the
> results they published aren't mind blowing like these.
>
>
> 2017-12-19 9:15 GMT-06:00 Fidel Santiago :
>
>> Hello,
>>
>> I was thinking about this development and what it may mean from the point
>> of view of a more general AI. I daresay the next experiment would be to
>> have just one neural net playing the three games, right? To my
>> understanding we still have three instances of the same *methodology*
>> but not yet a single one playing different games.
>>
>> Best regards,
>>
>> Fidel Santiago.
>>
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Re: [Computer-go] AlphaGo Zero SGF - Free Use or Copyright?

[Marc Landgraf]

There is even a decent site for those situations:

http://moralmachine.mit.edu/ (select language and then click "start
judging")

2017-10-31 7:55 GMT+01:00 Petri Pitkanen :

> and we can allways come up with bizarre situation like casualties insidet
> the vehicle vs casualties to persoons outside the vehicle. I am pretty sure
> this will a long discussion with huge research gaps on ethics  as well as
> in engineering
>
> 2017-10-31 7:00 GMT+02:00 Robert Jasiek :
>
>> On 30.10.2017 19:22, Pierce T. Wetter III wrote:
>>
>>> this car and this child
>>>
>>
>> In Germany, an ethics commission has written ethical guidelines for
>> self-driving cars with also the rule to always prefer avoiding casualties
>> of human beings.
>>
>>
>> --
>> robert jasiek
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Re: [Computer-go] Alphago and solving Go

[Marc Landgraf]

I don't mind your terminology, in fact I feel like it is a good way to
distinguish the two different things. It is just that I considiered one
thing wrongly used instead of the other for the discussion here.

But if we go with the link you are suggesting here:
Shouldnt that number at most be 722^#positions? Since adding a black or a
white stone is something fundamentally different?

2017-08-09 20:50 GMT+02:00 John Tromp :

> > And what is the connection between the number of "positions" and the
> number
> > of games
>
> The number of games is at most 361^#positions.
>
> > or even solving games? In the game trees we do not care about
> > positions, but about situations.
>
> We care about lots of things, including intersections, stones,
> liberties, strings, positions, sets of previous positions.
>
> > I'm actually surprised that this "absurd" to you...
>
> I said that referring to a board configuration together with the set
> of all previously occurring board configurations (and turn to move) as
> "position" is absurd.
> We need a simple word to denote a board configuration, and "position" fits
> that requirement. A good word for all the relevant historical
> information leading up to a position is "situation".
>
> -John
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Re: [Computer-go] Alphago and solving Go

[Marc Landgraf]

And what is the connection between the number of "positions" and the number
of games or even solving games? In the game trees we do not care about
positions, but about situations. For the game tree it indeed matters whos
turn it is, which moves are legal, and if super-ko rules are used which
positions are legal and which aren't. It will be tough to solve the game
even for a single position without having this information.

I'm actually surprised that this "absurd" to you...

2017-08-09 17:48 GMT+02:00 John Tromp :

> > Under which ruleset is the 3^(n*n) a trivial upper bound for the number
> of
> > legal positions?
>
> Under all rulesets.
>
> > Unless we talk about simply the visual aspect
>
> Yes, we do.
>
> > but then this has
> > absolutely nothing to do with the discussion abour solving games.
>
> If you want the notion of "position" to encode everything needed to
> determine legality of future plays, then in the case of superko, you
> need the entire set of previous board configurations, which to me is
> rather absurd.
> Instead you should call that "situation".
> That's how we distinguish the two flavors of superko;
> positional vs situational...
>
> regards,
> -John
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Re: [Computer-go] Alphago and solving Go

[Marc Landgraf]

Under which ruleset is the 3^(n*n) a trivial upper bound for the number of
legal positions?
I'm sure there are rulesets, under which this bonds holds, but I doubt that
this can be considered trivial.

Under the in computer go more common rulesets this upper bound is simply
wrong. Unless we talk about simply the visual aspect, but then this has
absolutely nothing to do with the discussion abour solving games.

2017-08-09 14:34 GMT+02:00 Gunnar Farnebäck :

> Except 361! (~10^768) couldn't plausibly be an estimate of the number of
> legal positions, since ignoring the rules in that case gives the trivial
> upper bound of 3^361 (~10^172).
>
> More likely it is a very, very bad attempt at estimating the number of
> games. Even with the extremely unsharp bound given in
> https://tromp.github.io/go/gostate.pdf
>
> 10^(10^48) < number of games < 10^(10^171)
>
> the 361! estimate comes nowhere close to that interval.
>
> /Gunnar
>
> On 08/07/2017 04:14 AM, David Doshay wrote:
>
>> Yes, that zeroth order number (the one you get to without any thinking
>> about how the game’s rules affect the calculation) is outdated since early
>> last year when this result gave us the exact number of legal board
>> positions:
>>
>> https://tromp.github.io/go/legal.html
>>
>> So, a complete game tree for 19x19 Go would contain about 2.08 * 10^170
>> unique nodes (see the paper for all 171 digits) but some number of
>> duplicates of those nodes for the different paths to each legal position.
>>
>> In an unfortunate bit of timing, it seems that many people missed this
>> result because of the Alpha Go news.
>>
>> Cheers,
>> David G Doshay
>>
>> ddos...@mac.com 
>>
>>
>>
>>
>>
>> On 6, Aug 2017, at 3:17 PM, Gunnar Farnebäck >> > wrote:
>>>
>>> On 08/06/2017 04:39 PM, Vincent Richard wrote:
>>>
 No, simply because there are way to many possibilities in the game,
 roughly (19x19)!

>>>
>>> Can we lay this particular number to rest? Not that "possibilities in
>>> the game" is very well defined (what does it even mean?) but the number of
>>> permutations of 19x19 points has no meaningful connection to the game of go
>>> at all, not even "roughly".
>>>
>>> /Gunnar
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Re: [Computer-go] mini-max with Policy and Value network

[Marc Landgraf]

And talkig about tactical mistakes:
Another game, where a trick joseki early in the game (top right) completely
fools Leela. Leela here play this like it would be done in similar shapes,
but then gets completely blindsided. But to make things worse, it finds the
one way to make the loss the biggest. (note: this is not reliable when
trying this trick joseki, Leela will often lose the 4/5 stones on the left,
but will at least take the one stone on top in sente instead of screwing up
like it did here) Generally this "trick" is not that deep reading wise, but
given its similarity to more common shapes I can understand how the bot
falls for it.
Anyway, Leela manages to fully stabilize the game (given our general
difference in strength, this should come as no surprise), just to throw
away the center group.

But what you should really look at here is Leelas evaluation of the game.

Even very late in the game, the MC part of Leela considers Leela well
ahead, completely misreading the L+D here. Usually in most games Leela
loses to me, the issue comes the other way around. Leela NN strongly
believes in the game to be won, while the MC-part notices the real trouble.
But not here. Now of course this kind of misjudgement also could serve as
explaination how this group could die in first place.

But having had my own MC-Bot I really wonder how it could misevaluate so
badly here. To really lose this game as Black it either requires
substantial self ataris by Black, or large unanswered self atari by White.
Does Leela have such light playouts that those groups can really flip
status in 60%+ of the MC-Evaluations?

2017-05-22 20:46 GMT+02:00 Marc Landgraf <mahrgel...@gmail.com>:

> Leela has surprisingly large tactical holes. Right now it is throwing a
> good number of games against me in completely won endgames by fumbling away
> entirely alive groups.
>
> As an example I attached one game of myself (3d), even vs Leela10 @7d. But
> this really isn't a onetime occurence.
>
> If you look around move 150, the game is completely over by human
> standards as well as Leelas evaluation (Leela will give itself >80% here)
> But then Leela starts doing weird things.
> 186 is a minor mistake, but itself does not yet throw the game. But it is
> the start of series of bad turns.
> 236 then is a non-threat in a Ko fight, and checking Leelas evaluation,
> Leela doesn't even consider the possibility of it being ignored. This is
> btw a common topic with Leela in ko fights - it does not look at all at
> what happens if the Ko threat is ignored.
> 238 follows up the "ko threat", but this move isn't doing anything either!
> So Leela passed twice now.
> Suddenly there is some Ko appearing at the top right.
> Leela plays this Ko fight in some suboptimal way, not fully utilizing
> local ko threats, but this is a concept rather difficult to grasp for AIs
> afaik.
> I can not 100% judge whether ignoring the black threat of 253 is correct
> for Leela, I have some doubts on this one too.
> With 253 ignored, the game is now heavily swinging, but to my judgement,
> playing the hane instead of 256 would still keep it rather close and I'm
> not 100% sure who would win it now. But Leela decides to completely bury
> itself here with 256, while giving itself still 70% to win.
> As slowly realization of the real game state kicks in, the rest of the
> game is then the usual MC-throw away style we have known for years.
>
> Still... in this game you can see how a series of massive tactical
> blunders leads to throwing a completely won game. And this is just one of
> many examples. And it can not be all pinned on Ko's. I have seen a fair
> number of games where Leela does similar mistakes without Ko involved, even
> though Ko's drastically increase Leelas fumble chance.
> At the same time, Leela is completely and utterly outplaying me on a
> strategical level and whenever it manages to not make screwups like the
> ones shown I stand no chance at all. Even 3 stones is a serious challenge
> for me then. But those mistakes are common enough to keep me around even.
>
> 2017-05-22 17:47 GMT+02:00 Erik van der Werf <erikvanderw...@gmail.com>:
>
>> On Mon, May 22, 2017 at 3:56 PM, Gian-Carlo Pascutto <g...@sjeng.org>
>> wrote:
>>
>>> On 22-05-17 11:27, Erik van der Werf wrote:
>>> > On Mon, May 22, 2017 at 10:08 AM, Gian-Carlo Pascutto <g...@sjeng.org
>>> > <mailto:g...@sjeng.org>> wrote:
>>> >
>>> > ... This heavy pruning
>>> > by the policy network OTOH seems to be an issue for me. My program
>>> has
>>> > big tactical holes.
>>> >
>>> >
>>> > Do you do any hard pruning? My engines (Steenvreter,Magog) always h

Re: [Computer-go] mini-max with Policy and Value network

[Marc Landgraf]

Leela has surprisingly large tactical holes. Right now it is throwing a
good number of games against me in completely won endgames by fumbling away
entirely alive groups.

As an example I attached one game of myself (3d), even vs Leela10 @7d. But
this really isn't a onetime occurence.

If you look around move 150, the game is completely over by human standards
as well as Leelas evaluation (Leela will give itself >80% here)
But then Leela starts doing weird things.
186 is a minor mistake, but itself does not yet throw the game. But it is
the start of series of bad turns.
236 then is a non-threat in a Ko fight, and checking Leelas evaluation,
Leela doesn't even consider the possibility of it being ignored. This is
btw a common topic with Leela in ko fights - it does not look at all at
what happens if the Ko threat is ignored.
238 follows up the "ko threat", but this move isn't doing anything either!
So Leela passed twice now.
Suddenly there is some Ko appearing at the top right.
Leela plays this Ko fight in some suboptimal way, not fully utilizing local
ko threats, but this is a concept rather difficult to grasp for AIs afaik.
I can not 100% judge whether ignoring the black threat of 253 is correct
for Leela, I have some doubts on this one too.
With 253 ignored, the game is now heavily swinging, but to my judgement,
playing the hane instead of 256 would still keep it rather close and I'm
not 100% sure who would win it now. But Leela decides to completely bury
itself here with 256, while giving itself still 70% to win.
As slowly realization of the real game state kicks in, the rest of the game
is then the usual MC-throw away style we have known for years.

Still... in this game you can see how a series of massive tactical blunders
leads to throwing a completely won game. And this is just one of many
examples. And it can not be all pinned on Ko's. I have seen a fair number
of games where Leela does similar mistakes without Ko involved, even though
Ko's drastically increase Leelas fumble chance.
At the same time, Leela is completely and utterly outplaying me on a
strategical level and whenever it manages to not make screwups like the
ones shown I stand no chance at all. Even 3 stones is a serious challenge
for me then. But those mistakes are common enough to keep me around even.

2017-05-22 17:47 GMT+02:00 Erik van der Werf :

> On Mon, May 22, 2017 at 3:56 PM, Gian-Carlo Pascutto 
> wrote:
>
>> On 22-05-17 11:27, Erik van der Werf wrote:
>> > On Mon, May 22, 2017 at 10:08 AM, Gian-Carlo Pascutto > > > wrote:
>> >
>> > ... This heavy pruning
>> > by the policy network OTOH seems to be an issue for me. My program
>> has
>> > big tactical holes.
>> >
>> >
>> > Do you do any hard pruning? My engines (Steenvreter,Magog) always had a
>> > move predictor (a.k.a. policy net), but I never saw the need to do hard
>> > pruning. Steenvreter uses the predictions to set priors, and it is very
>> > selective, but with infinite simulations eventually all potentially
>> > relevant moves will get sampled.
>>
>> With infinite simulations everything is easy :-)
>>
>> In practice moves with, say, a prior below 0.1% aren't going to get
>> searched, and I still regularly see positions where they're the winning
>> move, especially with tactics on the board.
>>
>> Enforcing the search to be wider without losing playing strength appears
>> to be hard.
>>
>>
> Well, I think that's fundamental; you can't be wide and deep at the same
> time, but at least you can chose an algorithm that (eventually) explores
> all directions.
>
> BTW I'm a bit surprised that you are still able to find 'big tactical
> holes' with Leela now playing as 8d KGS
>
> Best,
> Erik
>
>
>
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leela10screwup.sgf
Description: application/go-sgf
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Re: [Computer-go] sgf files for world go championship games?

[Marc Landgraf]

if you have an account at go4go, you can view them there, e.g. the Zen
games are here:
http://www.go4go.net/go/games/byplayer/1776

2017-03-24 17:55 GMT+01:00 Ray Tayek :

> any one got a pointer to these?
>
> 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] it's alphago

[Marc Landgraf]

And why would it be desirable that 'the big corporate players lose interest
to devote computer power'?
And who are those big corporate players? Deepmind? Who are not even selling
their bot? Or are you talking about CS/Zen who are having indeed financial
interests here?
What would be the benefit of any of those parties in losing interest?

Am 06.01.2017 17:22 schrieb "daniel rich" :

> 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 <
> lukas.drinkt.t...@gmail.com> 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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Re: [Computer-go] Go Tournament with hinteresting rules

[Marc Landgraf]

Well, the system i identical to that besides:
- there are 19 bonus points for winning (I really like that one...)
- it is capped at +40 an -40

But I do not think it is too interesting for bots right now mostly due to
lack of similar strengths bots. And while the GtI tourney does equalize
this with handicap, the way bots deal with handi, especially when being of
different strengths, kinda ruins it.

2016-12-09 0:11 GMT+01:00 Lukas van de Wiel :

> So why not add the amount of points equal to your score? You win by 16.5
> points? You get 16.5 points. You lose by 4.5. You lose 4.5 points. At the
> end of the tournament, there will be contestants with a negative score, but
> it seems a more straightforward system to me, and the players losing only
> by a small amount will still rate righter than those that are slaughtered.
>
> On Thu, Dec 8, 2016 at 11:56 PM, Erik van der Werf <
> erikvanderw...@gmail.com> wrote:
>
>> On Thu, Dec 8, 2016 at 10:58 PM, "Ingo Althöfer" <3-hirn-ver...@gmx.de>
>> wrote:
>> > Playing under such conditions might be a challenge for the bots
>>
>> Why? Do you think the humans will collude?  ;-)
>>
>> Erik.
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Re: [Computer-go] Auto Go game recorder

[Marc Landgraf]

Hi,
I could suggest BadukCap, which I have seen reasonably good reviews for.

https://itunes.apple.com/de/app/baduk-cap/id896353586?mt=8 (it links the
German appstore, but should be available globally and has all kinds of
languages)

Best, Marc

2016-11-25 1:16 GMT+01:00 Hideki Kato :

> Hello everybody,
>
> Chizu Kobayashi 6p is seeking automatic Go game recorders.  Does
> anyone know about that?  An application for mobilephones is the best
> but any system is appriciated.
>
> Best,
> Hideki
> --
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Re: [Computer-go] DarkForest is open-source now.

[Marc Landgraf]

This GPL discussion just shows why we hardly ever get such amazing
insight... Which is pretty sad. :/ I hope it neither discourages the
FAIR team nor others from still publishing their stuff in the future.

2016-06-11 0:05 GMT+02:00 uurtamo :
> GPL is rough
>
> On Jun 10, 2016 2:02 PM, "Xavier Combelle" 
> wrote:
>>
>> 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] Crazystone on Steam

[Marc Landgraf]

Oh, this is great.
But one question: Will there be any updates/upgrades/patches for client
and/or engine?
Having it on Steam would finally give a convenient way to do so. This would
be a reason for me to buy it, hoping for it to change for the better. Or
will it just be a one release policy? Can't see myself spending 74euro
then, considering the mixed reviews in various places.
Am 27.05.2016 21:09 schrieb "Jim O'Flaherty" :

> Tysvm! The video on Stream is a very nice touch. And the first review
> rocks!
>
> On Fri, May 27, 2016 at 1:36 PM, Andreas Persson 
> wrote:
>
>> Congrats on the Steam release of Crazystone Rémi! Hope it will sell well.
>> For people that haven't seen it here is a link
>> http://store.steampowered.com/app/479330/. Bit to pricey for me but will
>> pick it up at some point, enjoy the ios app a lot.
>>
>> /Andreas
>>
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Re: [Computer-go] BetaGo?

[Marc Landgraf]

When the AlphaGo hype was in full force, there were discussion about
similar matches in Japan and China. DeepZen vs Iyama and a bot by a
team/company called Novumind vs Ke Jie. Now the email adress for
reports about BetaGo links to exactly this Novumind.
So I would consider it as this Chinese superbot attempt. (with a
hillariously plagiarized name)

Ingo also tried to look at it in the German Go forum (in German:
http://www.dgob.de/yabbse/index.php?topic=6036.msg200586#msg200586 )
and found the following linked.ln account who may be part of this:
https://de.linkedin.com/in/ren-wu-944162?trk=pub-pbmap

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] Operators for Frisbee Go Simulation

[Marc Landgraf]

Goncalos were on 7th of April. Just copying them here:
---
On frisbee Go itself I used the following definition:
1. An intended play must be legal -- no playing on top of a stone hoping
it 'falls' to the neighbor positions.
2. Unintentional plays that are illegal are nulled and don't imply a
desire to end the match.
3. The distribution of unintentional plays around the 4 neighbors is
constant even at the border where there are never 4 neighbors; "You hit
the target with prob. p, and its 4 neighbours with probability
(1-p)/4.". The residual probability at the border is not reused for
on-board plays.
4. Probability parameter p cannot be changed midgame, for simplification.
5. Technically, using the GTP, I assumed genmove_reg+play commands are
used, instead of genmove+undo+play or something frisbee specific. This
is probably stating the obvious.
---
Also your example for counting looked correct.

Also when trying to theoretically solve even very simple L+D
situations this game feels quite tricky for me and very different from
Go. Btw, for the theoretical question posed earlier (regarding the 3
point eye) I assumed the p to be the chance of deviation. Not the
chance to hit the target. So it would A) p=0.2, B) p=0.24, C) p=0.28
if we use p the way Goncalo defined it.

2016-04-12 13:29 GMT+02:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:
> Hello MArc,
>
> thanks for your contribution.
>
>> I still haven't seen an exactly specified ruleset for this game.
>> Goncalo made some assumptions earlier, which were not yet confirmed.
>
> Oh, from what day is his posting?
>
>
>> Also I would strongly recommend to not have any clearup-methods
>> allowed, but all positions have to be cleared up by "hand" and all
>> stones on the board in the end are considered alive.
>
> Interesting proposal.
> To make sure that I understood you correctly, let's count an artificial 
> example
> position (on 9x9-board) where both players have passed intentionally:
>
> Final position:
> . x o . o . o . .
> o o o o o . . o .
> . . . . . . o . o
> x x x x . . o o x
> . . . x . o o x x
> . . . x . o . x .
> . . . x . o . x .
> x x x x . o o x o
> . . . . . . . x .
>
> "Chinese style counting" with all stones considered alive:
> - X O O O - O O O
> O O O O O - - O O
> - - - - - - O O O
> X X X X - - O O X
> X X X X - O O X X
> X X X X - O - X -
> X X X X - O - X -
> X X X X - O O X O
> - - - - - - - X -
>
> Cells with X count for player x,
> cells with O counts for player o,
> cells with - are neutral.
>
> Did I get it correctly?
>
> Ingo.
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Re: [Computer-go] Operators for Frisbee Go Simulation

[Marc Landgraf]

I still haven't seen an exactly specified ruleset for this game.
Goncalo made some assumptions earlier, which were not yet confirmed.
Also I would strongly recommend to not have any clearup-methods
allowed, but all positions have to be cleared up by "hand" and all
stones on the board in the end are considered alive.

Oh, and for the theorists:
Assumptions:
- You have a group with a single 3 point eye.
- defender moves first.
a) it has "more than enough" outside liberties.
b) it has no outside liberties

and you use
A) p=0.20
B) p=0.19
C) p=0.18



What is the ideal strategy for both players in all the possible
positions? And considering those strategies, what is the likelyhood of
the group surviving?
It is obvious, that if either of both players hits the centre, the
position is won for him. Also if the defender accidentally hits one
end and reduces it to a 2 point eye, he has lost.
The difference between a) and b) is only the situation where the
attacker accidentally filled both ends of the eye. In a) playing in
the centre would be suicide, and given enough time the defender will
obviously win that situation. In b) you have a race for the centre.
If you assume A) the strategy looks quite simple. In the original
position both sides would throw at an end, hoping for it to land in
the centre instead. If it lands on the end for the defender, he would
have just killed his group (unfortunately), if it lands there for the
attacker, the game would go on and we have a race.
But what happens with B) and C)? Now the situation at the original
position isn't as trivial anymore. Aboves strategy is suddenly worse
now. No matter how you throw, it is more likely to hit an end than the
centre. Could you even create a situation where it is ideal for the
defender to pass and hope for the attacker to miss, before the
defender plays again?

2016-04-10 22:55 GMT+02:00 "Ingo Althöfer" <3-hirn-ver...@gmx.de>:
> Hello,
>
>> There is no way in GTP to distinguish intentional from unintentional
>> passes, so I suppose the simplest way is to perform things manually.
>
> Manually would mean. In each situation the followiong has to happen:
> (i) The program to move proposes a move x or a PASS.
> (ii) In case of a move x a die is rolled to decide to which place y on the 
> board
> this move goes. y is returned to the program and to the opposing program.
> In case of an unintended pass this information has to be returned to both 
> programs.
> (iii) In case of an intended PASS tis is transmitted to the other program.
>
> So: the programs for this manual mode need to have the possibility
> to enter move, unintended passes, and intended passes. Of course also
> takeback of moves should be possible to deal with operator errors.
>
>> Maybe it is an informal tournament and time controls don't really matter.
>
> There should be some time limit like 30 seconds or 1 minute per move (for
> the process that leads to proposal x or PASS) - and the operators should give
> their word of honor that they have implemented this.
>
> By the way: All programs should run on (more or less) identical hardware.
>
>
>> Also, do these remote participations come at a fee? :-)
>
> Of course: 50 Euro when a programmer participates "only" in Frisbee Go 
> simulation.
> And 25 Euro only, when Frisbee Go simulation is his second or third or ... 
> game in
> this Olympiad.
>
> Ingo.
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Re: [Computer-go] Game 4: a rare insight

[Marc Landgraf]

What is the most interesting part is, that at this point many pro
commentators found a lot of aji, but did not find a "solution" for Lee
Sedol that broke AlphaGos position. So the question remains: Did
AlphaGo find a hole in it's own position and tried to dodge that? Was
it too strong for its own good? Or was it a misevaluation due to the
immense amounts of aji, which would not result in harm, if played
properly?


2016-03-13 9:54 GMT+01:00 Darren Cook :
> From Demis Hassabis:
>   When I say 'thought' and 'realisation' I just mean the output of
>   #AlphaGo value net. It was around 70% at move 79 and then dived
>   on move 87
>
>   https://twitter.com/demishassabis/status/708934687926804482
>
> Assuming that is an MCTS estimate of winning probability, that 70%
> sounds high (i.e. very confident); when I was doing the computer-human
> team experiments, on 9x9, with three MCTS programs, I generally knew I'd
> found a winning move when the percentages moved from the 48-52% range
> to, say, 55%.
>
> I really hope they reveal the win estimates for each move of the 5
> games. It will especially be interesting to then compare that to the
> other leading MCTS programs.
>
> Darren
>
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Re: [Computer-go] AlphaGo won first game!

[Marc Landgraf]

Btw, is there any information on what hardware AlphaGo is running. And
how does it compare to the version used against Fan Hui?

2016-03-09 9:31 GMT+01:00 David Fotland :
> Many Faces thought alpha go was ahead most of the game.  It looked to me
> like the turning point was when Alphago cut in the center then gave up the
> two cutting stones for gains on both sides (but not so strong…).
>
>
>
> Congratulations Aja!
>
>
>
> I watched it at Google in Mountain View with about 100 people.
>
>
>
> David
>
>
>
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
> Jim O'Flaherty
> Sent: Tuesday, March 08, 2016 11:50 PM
> To: computer-go@computer-go.org
> Subject: Re: [Computer-go] AlphaGo won first game!
>
>
>
> 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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Re: [Computer-go] AlphaGo won first game!

[Marc Landgraf]

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

[Marc Landgraf]

What? You have mixed up things.

http://www.europeangodatabase.eu/EGD/Player_Card.php?=17374016

2016-02-02 20:21 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.
>
>
> Thanks, this point is interesting - that's really an argument limiting the
> strength of AlphaGo.
>
> On the other hand, they have super strong people in the team (at the pro
> level, maybe ? if Aja has pro level...),
> and one of the guys said he is "quietly confident", which suggests they have
> strong reasons for believing they have a big chance :-)
>
> Good luck AlphaGo :-) I'm grateful because since this happened many more
> doors are opened for people
> working with these tools, even if they don't touch games, and this is really
> useful for the world :-)
>
>
>
> --
> =
> "I will never sign a document with logos in black & white." A. Einstein
> Olivier Teytaud, olivier.teyt...@inria.fr, http://www.slideshare.net/teytaud
>
>
>
>
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Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

[Marc Landgraf]

You must be kidding about Lee Sedol.

Yes, he is not as dominating as before. (is it because he is weaker or
because the other ones got better?)
But he is still #3 in Korea having only dropped there this month,
being #2 for most of the last year. (btw overtaken by Park Younghoon,
who is not really a younger generation)
In Korea usually the Myeongin and Kuksu are considered the most
important titles. He owns one currently, dropped out in Semifinal in
the other one. Looking at the last big global title(MLily Cup), he
lost 2-3 in the final.

Not sure what you are asking for, but in pretty much any metric, only
Park Junghwan can currently be considered stronger in Korea, and less
than a handful players can be considered even with Lee Sedol.
And even worldwide... Yes, there is Ke Jie. Who else? Again you won't
find more than a handful Chinese players. And the performance of
Japanese/Taiwanese players on international level can easily be
ignored.

So in the end, we have 2 candidates, who may be strength wise a better
choice than Lee Sedol.
But then there is of course name value, legacy etc. Lee Sedol
represented and dominated the Go scene for more than a decade. And
neither Ke Jie nor Park Junghwan come even close to that, even with
their great performances in the last 1-3 years. There is a reason why
people asked for a Jubango involving Lee Sedol, and neither of the new
"kids".

So he was by far the biggest fish Google could ever catch for that
game, for Go insiders as well as for people outside the Go scene.

2016-01-31 21:49 GMT+01:00 Cai Gengyang :
> Hi all,
>
> Just to introduce myself, I am a 5 dan amateur Go player from Singapore who
> has recently taken an interest in computer Go, programming and AI and a
> newbie to these forums. Been playing Go for many years (perhaps 20 years or
> so and represented Singapore in the past, winning 3rd place at the World
> Youth Amateur Go Tournament in Hawaii and 12th place at the World Amateur Go
> Tournament in Japan) though I have not competed for many years. The recent
> improvement in Go AI has rekindled my interest in this ancient game and also
> artificial intelligence. I am genuinely fascinated that Go AI has advanced
> so far --- when I first started out, the best Go AI was 30 kyu or so and
> nobody believed that they could even defeat a strong amateur Go player. Then
> came programs like ZenBS and CrazyStone , which surprised me because I could
> only win perhaps 50% of the time against these programs. The result of
> AlphaGo has shocked me to the core --- I don't know whether to feel happy or
> sad. A sudden impending sense of doom --- that mankind has been eclipsed.
> Just a few points I thought to raise here ...
>
>
>
> Regarding the number of legal Go positions for a 19x19 board , it is ~2.082
> x 10^170 (from the Sensei's website)
>
> Number of legal positions
>
> One number of interest for use in calculations of the number of possible
> games is the number of legal positions. An upper bound of the number of
> positions on a 19x19 go board is not hard to calculate. Every intersection
> can be either black, white, or empty, so the number of possible positions is
> exactly 3^361, which is ~1.741 × 10^172. For this bound, symmetry is not
> accounted.
>
> However, many of these positions contain strings of stones without liberties
> and therefore are not legal. The exact number of legal positions has been
> calculated for square boards up to size 19×19 by Tromp and others[1][2].
>
> Some numbers:
>
> 9×9 board: ~1.039 × 10^38
> 13×13 board: ~3.724 × 10^79
> 17×17 board: ~1.908 × 10^137
> 19×19 board: ~2.082 × 10^170 (i.e., a 2 followed by 170 zeroes)
>
> For the 19×19 board, the number of legal positions is about 1.196% of the
> possible positions[3].
>
>
> Regarding how significant the victory is , it is important to note that (no
> disrespect intended) :
>
> 1) Fan Hui is no longer in his prime competitive Go playing games
>
> 2) Fan Hui is not a top tier professional Go player ( far from it )
>
> 3) There was no significant monetary incentive at stake
>
> It will be interesting to see how the program fares against Lee Sedol ...
> but then again Lee Sedol is no longer in his prime playing days , though
> there is a million dollar prize incentive.
>
> I think what they should do is pit the program against the reigning world Go
> champion when he or she is in the prime Go playing days (late teens to mid
> 20's)
>
>
> Regards,
>
> Cai Gengyang
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Re: [Computer-go] Computer-go Digest, Vol 72, Issue 41

[Marc Landgraf]

Why would they water down their Lee Sedol game by announcing another
game before their big game has even happened? No matter if that game
would be before or after.
Sounds like an awful PR strategy.

2016-02-01 2:51 GMT+01:00 uurtamo . :
> It might even be interesting if it took place *before* the lee sedol match.
>
> s.
>
> On Jan 31, 2016 5:09 PM, "Chaohao Pan"  wrote:
>>
>> Just in case that no one knows it. Ke Jie has publicly announced that he
>> is willing to play against AlphaGo, even without any prize money. Since Ke
>> Jie is absolutely the current No.1, it would be a good choice to have
>> another match with Ke Jie, time permitting, no matter AlphaGo wins or loses
>> against Lee Sedol,.
>>
>> Chaohao
>>
>> 2016-01-31 13:34 GMT-08:00 John Tromp :
>>>
>>> > You must be kidding about Lee Sedol.
>>> > ...
>>> > So he was by far the biggest fish Google could ever catch for that
>>> > game, for Go insiders as well as for people outside the Go scene.
>>>
>>> Well said, Marc.
>>>
>>> In terms of name recognition and domination in the past decade,
>>> who else but Lee Sedol should be picked as the "Kasparov of Go"
>>> in the ultimate Man vs Machine match?
>>>
>>> -John
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Re: [Computer-go] Game Over

[Marc Landgraf]

for those looking for sgfs: http://deepmind.com/alpha-go.html

2016-01-27 19:25 GMT+01:00 Julian Schrittwieser :

> Actually the paper has been in the works for quite a while and was already
> set to be released today for some weeks.
> It seems a journalist reached out to Facebook to comment a day ago.
>
> On Wed, Jan 27, 2016 at 6:19 PM, Gian-Carlo Pascutto 
> wrote:
>
>> On 27/01/2016 18:58, Darren Cook wrote:
>> > P.S. Curiously the BBC ran an article today on how Facebook is getting
>> > close to top pro level too:
>> http://www.bbc.co.uk/news/technology-35419141
>>
>>
>> http://googleresearch.blogspot.be/2016/01/alphago-mastering-ancient-game-of-go.html
>>
>> "The match was played behind closed doors between October 5-9 last year."
>>
>> They already achieved this a while ago. The Google announcement looks
>> like a direct response to the press Facebook was getting.
>>
>> --
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Re: [Computer-go] Facebook Go

[Marc Landgraf]

http://wayt.synology.me/wordpress/1348-2/
no handicap

2016-01-27 17:42 GMT+01:00 John Tromp :

> > A member of the German forum said, that a French Go player reported on
> > Facebook, that Fan Hui lost 5 out of 5 games to the Google Go engine.
>
> To ask the obvious:
>
> Were these even or handicap games?
>
> -John
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Re: [Computer-go] 7x7 Go is weakly solved

[Marc Landgraf]

Google translate on the article tells, that there is no algorithm, but that
they combined human and computer power on a larger scale to explore all
variations. It can't be proven that the result is correct, but the
likelihood is ~100%.

2015-11-30 13:20 GMT+01:00 Erik van der Werf :

> On Mon, Nov 30, 2015 at 12:52 PM, Aja Huang  wrote:
>
>> Hi Erik,
>>
>> On Mon, Nov 30, 2015 at 10:37 AM, Erik van der Werf <
>> erikvanderw...@gmail.com> wrote:
>>
>>> Hi Aja,
>>>
>>> This result seems consistent with earlier claimed human solutions for
>>> 7x7 dating back to 1989. So what exactly is new? Did he write a program
>>> that actually calculates the value?
>>>
>>
>> Did you mean 7x7 Go was weakly solved before?
>>
>
> It depends on what you mean by 'weakly solved'. If we take the definition
> from https://en.wikipedia.org/wiki/Solved_game:
>
> *'Provide an algorithm that secures a win for one player, or a draw for
> either, against any possible moves by the opponent, from the beginning of
> the game.'*
>
> then no, I did not mean that, and that's why I asked you if he actually
> wrote a program that does this for 7x7.
>
> Strong humans players including some pro's claimed to have solved 7x7
> already back in 1989 (see my phd thesis for a reference), but AFAIK they
> did not implement an algorithm, so just like most of the other small board
> results by humans these were never really proofs in a strict sense.
>
> Best,
> Erik
>
>
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[Computer-go] Theoretical question

[Marc Landgraf]

Hi,
there is a question that lately crossed my mind. Considering an nxn Go
board, no suicide allowed and with a rule that does not allow repetition of
a position, unless caused by a single pass:
What is the maximum number of board positions that can be run through in a
single sequence starting from an empty board?
Different wording: What is the maximum game length if not counting passes?

It can easily be proven that for n>=3 this number must be lower than the
number of legal positions. I didn't check for n=2, simple bruteforce would
probably solve that.

What happens to the ratio between the number of legal positions and the
longest possible sequence with growing n?

Is this already known? Or does anyone have a clue how to figure it out?

 ~Marc
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Re: [Computer-go] alarming UCT behavior

[Marc Landgraf]

It is indeed very realistic and can be recreated in Go.

The issue is, that you are chopping of the tree at a fixed point and this
may heavily bias the the entire tree, if this point influences the playout.
Like imagine there is one big Atari in the entire game, but it can be
easily answered. If you have a single path in your tree that ends with said
Atari, while no other path ends with this Atari played (or the Atari is
already answered in the tree), this path will then dominate all other
pathes, because in this path the likelyhood of winning (by taking the
stones in Atari) is much bigger than in all other branches. But if you
would chop off the last move that branch suddenly the evaluation would be
correct. If you would not do any more playouts on this node, it would be
just one biased playout, not a big deal in the grand scale. If you would
allow the MCTS to continue further, it would also be able to refute the
Atari. But you are stopping right at the atari, and then pile on playouts
that make it seem work.


2015-11-06 19:59 GMT+01:00 Gonçalo Mendes Ferreira :

> That doesn't seem very realistic. I'd guess your prior values are accurate
> but the simulations are biased or not representative. Or you miss precision
> in your transition quality floating points. Or there's a bug related to
> being an adversarial problem and you didn't have the robots swap colors? :)
>
> Do tell what it was when you discover the problem.
>
> Gonçalo F.
>
>
> On 06/11/2015 18:48, Dave Dyer wrote:
>
>> Developing a UCT robot for a new game, I have encountered a
>> surprising and alarming behavior:  the longer think time the
>> robot is given, the worse the results.  That is, the same robot
>> given 5 seconds per move defeats one give 30 seconds, or 180 seconds.
>>
>> I'm still investigating, but the proximate cause seems to be
>> my limit on the size of the UCT tree.   As a memory conservation
>> measure, I have a hard limit on the size of the stored tree. After
>> the limit is reached, the robot continues running simulations, refining
>> the outcomes based on the existing tree and random playouts below
>> the leaf nodes.
>>
>> My intuition would be that the search would be less effective in this
>> mode, but producing worse results (as measured by self-play) is
>> strongly counter intuitive.
>>
>> Does it apply to Go?  Maybe not, but it's at least an indicator
>> that arbitrary decisions that "ought to" be ok can be very bad in
>> practice.
>>
>>
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Re: [Computer-go] Facebook Go AI

[Marc Landgraf]

then again, Gnugo donked that game pretty badly.
Showing one game, where Gnugo just throws away the entire top before move
50 is not really telling about the overall strength, imho. Gnugo repeats
the failure by suiciding the top right as well.
What is shown after is hard to evaluate, considering the score difference.
The fact that the FB Bot prefers to play random moves in the center instead
of removing the possible Ko in the lower left later is weird, but may be
due to it's gigantic lead at this point.

Another interesting thing to note is, that the values shown on the right do
not always correspond to the played moves. E.g. at move S17 (killing the
top right) actually S16 had been give a higher score then the played S17

2015-11-03 17:22 GMT+01:00 Aja Huang :

> Yes I checked the game. The agent looks pretty strong. It crushed GnuGo
> easily.
>
> Aja
>
> On Tue, Nov 3, 2015 at 4:20 PM, Rémi Coulom  wrote:
>
>> Can a strong player look at the video and give impressions about the game?
>>
>> On 11/03/2015 03:28 PM, Petr Baudis wrote:
>>
>>>Hi!
>>>
>>>Facebook is working on a Go AI too, now:
>>>
>>> https://www.facebook.com/Engineering/videos/10153621562717200/
>>> https://code.facebook.com/posts/1478523512478471
>>>
>>> http://www.wired.com/2015/11/facebook-is-aiming-its-ai-at-go-the-game-no-computer-can-crack/
>>>
>>> The way it's presented triggers my hype alerts, but nevertheless:
>>> does anyone know any details about this?  Most interestingly, how
>>> strong is it?
>>>
>>>
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Re: [Computer-go] KGS bot tournaments - what are your opinions?

[Marc Landgraf]

I still like the idea of "1 Desktop/Notebook" for the lowspec category.
And what is the point? Comparability. How are you comparing your "research
results" if it is not clear, if the advantage comes from an hardware
advantage or from your newly developed algorithms? If tried to improve the
aerodynamic of a Ferrari, you are also not proving that by racing against
some Skodas.
Of course, improving parallelization etc is an important part of Computer
Go Development, but sometimes it is nice to eliminate this factor to see
how the rest of the coders work is doing.


2015-10-10 19:30 GMT+02:00 David Doshay :

> I agree completely that there is no way to enforce computational limits
> over the internet.
>
> I am against ‘identical hardware’ tournaments because people have worked
> to get their programs working on the hardware they have, and some people
> will be on the other side of any hardware decision, Mac v.s. PC being the
> most obvious.
>
> I am left wondering what the point is for such a tournament. Is it to show
> who is the most efficient programmer? Is it to show how these programs
> might run on somebody’s home computer? These things are not important for
> research code that is not intended for resale.
>
> Cheers,
> David G Doshay
>
> ddos...@mac.com
>
>
>
>
>
> On 10, Oct 2015, at 8:33 AM, Peter Drake  wrote:
>
> I'm also for no limits, if only because there's no way to enforce them.
>
> If there is to be a limited division, I'd like to see all programs run on
> identical hardware.
>
> On Fri, Oct 9, 2015 at 6:07 AM, Hiroshi Yamashita 
> wrote:
>
>> Hi Nick,
>>
>> I'd like no limit. Restriction will lose a chance of massive
>> computer's programming. But one thread limit tournament
>> once a year may be interesting.
>>
>> I like (2), and (3) is nice, but I'm already happy with your reports!
>>
>> Regards,
>> Hiroshi Yamashita
>>
>>
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[Computer-go] Utilizing multiple parametersets/bias systems/bots

[Marc Landgraf]

Heya,
I lately tried to think about, whether it would be possible to combine the
strengths of different bots, or at least different parameter sets/bias
systems for one bots in some way. They may shine at different
situations/phases during the game, but how to figure out, which one is
currently the better one?

What I now came up with, was the following:
For simplicity we assume for now, that our different bots are using the
same playouts, but different approaches during the tree phase. So maybe
they use different ways to bias nodes, different selection formulas etc.
Gonna focus on them using different bias systems.
Now you split up your playouts in percentiles:
25%: Bot1 selects the white moves, Bot2 the black ones
25%: the other way around
0x%50%: Bot1 selects for both.
50%-x%: Bot2 selects for both.
You track the win rates of those first 2 quarters separately and also
calculate winrate of Bot1 vs Bot2.
Now if the bots are identical obviously both should win 50%. But if the
bots are different, you may see different results.
E.g. when Bot1 wins 55% of his games, his move selection is probably better
then Bot2's move selection. Here you have to be careful about wrong
conclusions, because if you would setup a depth-first bot vs a width-first
you would certainly also get win rates heavily in favor of the depth-first.
But where this could shine is, when using different bias systems. Because
it actually tells you, which bias system is doing better in the current
board situation.
Now you can use that knowledge to calculate x. E.g. if either bot wins 60%+
he gains all 50% of the remaining playouts, and let the balance slide
linearly, if the win rate is 40-60% for the bots against each other. (use
whatever formula here, open for testing)

This should enable you to figure out on the fly, which bias system is doing
a better job at the current situation, while doing playouts. You are just
tracking some additional stats.

Of course, there are pros and cons to this method:
+ In general, switching selection method in the tree should not cost any
time, and tracking those additional stats also costs close to no time. Only
additional time used comes from using a second bias function or similar
(because now you have to calculate the bias twice, for most nodes)
- At the same time, the amount of data is actually increasing, because you
have to track the stats for the different bots. This may cause memory
issues! (but when using a distributed memory solution anyway, it does not
create additional data, if each memory/thread unit is assigned to one of
those percentiles of playouts)
+- In general the costs increase depends on how different the 2 Bots are.
If they are the same, there would be basically no cost.
+ Allows you to figure out, which bot/bias/selection is doing better right
now
- but may lead to false conclusions, like above mentioned depth vs width
example
+- As long as both bots are of similar strength, you should not lose from
using this kind of system. Worst case is, that you play the wrong bots
move, if you had above mentioned false conclusions. But when they are close
in strength, that is not worse than using just one bot all the time. Of
course, if one bot is dominating the other one in all situations, you are
losing quality, when figuring out again and again, that this is the case.
(because in 50% of the playouts, half of the moves were selected by the
worse bot)



So some quick ideas how it could be modified further:
- all percentages are obviously placeholders and could be adjusted (even
dynamically)
- assuming you have a low cost and a very high time cost bias function, you
can actually check, if it is worth using the high cost bias function, or if
the board situation is simple enough to churn out more playouts using the
cheaper function.
- identifying certain game situations: you may know, that one bias is doing
much better in corner fights or liberty races, so if that one is
dominating, you are probably in such a situation and can now adjust further
(e.g. modify playouts, add additional routines, whatever)
- using more then 2 routines, possibly in conjunction with the idea above
to identify situations.

Of course, one could now also think how to expand this idea, with using
different playouts, but I'm not sure how to judge, which playouts are the
better ones, when using different ones. Only because Bot1's playouts tell
me, I'm winning 60%, it does not mean, that his playouts are better then
Bot2's, who is only giving me 40%. (Even though I would wish so :D) So
right now I need the same playouts, to judge my selection routine. Maybe
someone else has an idea, how to judge playouts?


What do you guys think? Is there anything with potential in those ideas? Or
is the cost and danger of wrong conclusions too high for the possible gains?
Sadly can't test it with my own bot,as my versions strictly dominate each
other, and with its current strength it would probably be not conclusive
anyway.


Marc

Re: [Computer-go] [ANN] Imago - Go board optical recognition

[Marc Landgraf]

Out of curiosity...
In the picture you linked ( http://i59.tinypic.com/10cnu5c.jpg ), how does
your program read the position in the top left, considering the illegal
stone there?
Or does it not have any Go rules knowledge and leaves the interpretation to
the user? In that case it may create .sgf with illegal moves in it.
Considering during a regular game, you will actually snapshot illegal
positions a few times (when you take a picture between placing the stone
and removing the captures) I'm really wondering how your program handles it.

2015-01-17 14:18 GMT+01:00 Andrea Carta andrea.ca...@mclink.it:

 Hi mr. Baudis!

  we must have missed PhotoKifu when surveying the available software.

 We're getting used to that. In Italy Go is completely unknown and
 everything
 related goes unnoticed. Months ago I showed the program to a colleague of
 mine, who shares many of my hobbies, and he asked What is this? Chinese
 checkers?.

  There will be a scientific conference at EGC 2015 as well:
 
http://pasky.or.cz/iggsc2015/
 
  I think a presentation of paper describing your system would find a very
  interested audience.

 That's a fantastic new! After getting no reply from Sibiu last year we were
 afraid a second conference would never occur. We'll complete the paper soon
 and will attend the conference. We're already checking the accomodations in
 Liberec!

  we aimed to first test the software on series of events - in (well lit)
  Go clubs and on a larger tournament - starting the testing around now
  to have enough time to test it sufficiently and notice things like sun
  suddenly coming out of clouds, bumping the table, Go server streaming
  issues etc.

 We did an extensive test in December 2012, at the Firenze Go tournament
 (http://www.eurogotv.com/tournament/showresults.php?toernooicode=T121208B
 ).
 We realised our program (then version 1.0) only worked fine under good
 conditions - well lit environment, high point of view, limited number of
 disturbances in the pictures. We encountered dim lights (gobans' surfaces
 looked almost grey in the morning, completely grey in the afternoon),
 average points of view (40°-50° of elevation), and up to 60-70 pictures per
 game spoiled by fingers, hands, arms (sometimes of both players in the same
 picture, for example: http://i59.tinypic.com/10cnu5c.jpg).
 It took us a long time, but eventually we solved all these problems. We are
 now capable of analyzing without errors, taking just a bunch of
 milliseconds, even this kind of pictures:
 http://i61.tinypic.com/ak9zdv.jpg
 (this is probably the worst kind, speaking of sun suddendly coming out of
 the clouds)

  We also wanted to start talking around now to wms (KGS author) about
  a possibility of extending kgsGTP computer program interface of KGS
  to demonstration games.  I would recommend you to reserve enough time
  to working out these things too.

 Firenze's go players did ask us for such a thing (live feed on KGS). We're
 certainly going to work that out as soon as VideoKifu will be ready.

  I think a good time to start discussing this with them [EGC's organizers]
  would be after a first successful real-world tournament test (even on a
  small scale like single board).

 Of course we'll let you know about the Pisa testing. We're now contacting
 the organizers and will likely be able to take pictures of 3 or 4 games,
 and
 print the Kifus immediately afterwards (in a matter of minutes, we hope).

 Greetings and thanks for your interest!

 mr. Andrea Carta
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Re: [Computer-go] alternative for cgos

[Marc Landgraf]

I'm not convinced about that concept, tbh.
People put a lot of work to optimize their bots, include GPU usage and
figure out, how to use Pondering the best way. And then you want those
programmers to remove those features and put work into making their bots
run on your system, just to level the playing field.

2015-01-14 3:04 GMT+01:00 Chris LaRose cjlar...@gmail.com:

 Thanks everyone for all the feedback! Sorry I hijacked the thread!

 There are definitely some big pros and cons to a hosted, containerized
 environment for Go bots. I've replied to some comments below:

 Won’t hosting limit your usability?  With cgos I can build and immediately
 test on cgos on my development machine.  With your service, how do I get my
 new executable to run?


 You're absolutely right. The workflow for developers will have some
 additional overhead. The nice thing, though, is that Docker is very good at
 providing a way for an application to run virtually anywhere in a
 consistent fashion. That is, if you can get your bot running in a Docker
 container on your own machine, then it's pretty likely that it'll run on
 Baduk.io's Docker host without a hitch.

  If my engine uses a GPU or is a multinode cluster, how does that run on
 your docker service?


 Right now, I don't have plans for supporting such bots. But as Urban
 Hafner replied, it puts everyone on a level playing field--all bots have
 access to the same exact resources. As some have noted, maybe that means
 Baduk.io wouldn't serve the same purpose as CGOS. That's fine by me.

 At first, I want Baduk.io to be a place where players, especially
 beginners can get a few games in against bots. Because the bots are hosted,
 I'm not limited in the number of simultaneous games I can play against one
 bot. As far as I understand it, it GNU Go is playing against five different
 people on KGS, that means that there are five different instances of GNU Go
 running on people's machines someone in the world. If someone else wants to
 play, they can't. Also, bots that aren't currently playing a game can be
 terminated and won't consume resources. Starting and stopping containers is
 so fast that I can afford to only start bots immediately after its opponent
 plays, request a single move, and terminate it.

 Anyway, Baduk.io is largely only a proof-of-concept right now--I'll have
 to post a message to the list when I get a little further working on it.

 On Wed, Jan 14, 2015 at 8:33 AM, Joshua Shriver jshri...@gmail.com
 wrote:

 If you can send me a binary that would be greatly appreciated.  Trying
 to build some anchors now.

 -Josh

 On Tue, Jan 13, 2015 at 5:11 PM, Hideki Kato hideki_ka...@ybb.ne.jp
 wrote:
  Shilver,
 
  I'll be able to run FatMan1, the anchor for 9x9, on my site, if
  necessary.  Or, it's also possible to send you its binary and password
  so that you can run it on your site.
 
  Hideki
 
  Joshua Shriver: CAEdmgvYkFd-tDGL7TxYd55_bWpNBK4gk_VLBNHpWto=
 xwb5...@mail.gmail.com:
 I'll try and get CGOS back online before this weekend.  Technically it
 should be running now, but there were several issues.  In order to use
 it now  you take the cgos  client for your architecture and you have
 to specify cgos.computergo.org  manually since the binaries are
 hardcoded to the old boardspace address.  I've had some troubles
 unbundling the binaries and rebuilding the executables with TCL.
 
 Rankings are also an issue as well which is something I'll have to
 change in the code to make sure anchors and their predefined ELO
 ratings are used.
 
 Will try and make a better write-up on how to connect.  Hopefully this
 weekend I should have the anchors running 24/7 and some people can try
 connecting.
 
 I'll flush the old data and in terms of games and we'll start with a
 fresh slate.  Though all the data even from years past are still
 available though for historic reasons and for anyone who wants the
 SGF's.
 
 -Josh
 
 On Fri, Jan 9, 2015 at 5:47 AM, folkert folk...@vanheusden.com wrote:
  Hi,
 
  I have the feeling that cgos won't come back in even the distant
 future
  so I was wondering if there are any alternatives?
  E.g. a server that constantly lets go engines play against each other
  and then determines an elo rating for them.
 
 
  Folkert van Heusden
 
  --
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  In all these cases take a look at http://www.vanheusden.com/fi/ maybe
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