Re: [Computer-go] Building A Computer Go AI

Here is a simple working implementation. https://github.com/pasky/michi From the beginning of the readme: Michi --- Minimalistic Go MCTS Engine Michi aims to be a minimalistic but full-fledged Computer Go program based on state-of-art methods (Monte Carlo Tree Search) and written in Python. Our

Re: [Computer-go] Facebook Go AI

So the KGS bots darkforest and darkfores1 play with only DCNN, no MCTS search added? I wish they would put darkfores2 with MCTS on KGS, why not put your strongest bot out there? 2015-11-23 10:38 GMT-06:00 Petr Baudis : > The numbers look pretty impressive! So this DNN is as

Re: [Computer-go] Facebook Go AI

As of about an hour ago darkforest and darkfores1 have started playing rated games on KGS! 2015-11-23 11:28 GMT-06:00 Andy <andy.olsen...@gmail.com>: > So the KGS bots darkforest and darkfores1 play with only DCNN, no MCTS > search added? I wish they would put darkfores2 with MCTS

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

The Deepmind paper has a short section on the Rollout Policy they use, it looks like they made some improvements on for their rollouts, maybe they are better at handling semeai than previous methods. The response and non-response patterns sound similar, but they also include liberty counts. I

Re: [Computer-go] it's alphago

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.

Re: [Computer-go] action-value Q for unexpanded nodes

I am changing other variables at the same time. - Andy 2017-12-03 14:30 GMT-06:00 Álvaro Begué <alvaro.be...@gmail.com>: > The text in the appendix has the answer, in a paragraph titled "Expand and > evaluate (Fig. 2b)": > "[...] The leaf node is expanded and and

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

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

Re: [Computer-go] mcts and tactics

How do you interpret this quote from the AGZ paper? "Surprisingly, shicho (“ladder” capture sequences that may span the whole board) – one of the first elements of Go knowledge learned by humans – were only understood by AlphaGo Zero much later in training." To me "understood" means the neural

Re: [Computer-go] action-value Q for unexpanded nodes

bility distribution from the > policy output of the network, and over time it converges to using primarily > the moves that have best results. So the details of how Q is initialized > are not very relevant. > > > On Sun, Dec 3, 2017 at 5:11 PM, Andy <andy.olsen...@gmail.com>

[Computer-go] kgs-chat command not working

I'm trying to implement the kgs-chat command but I'm getting a crash in kgsGtp-3.5.22. Everything works fine and I can chat with the bot. But when a match starts that's when it crashes. Here is my gtp log. Any ideas? Or can someone paste me an example log that works or point me to example code?

[Computer-go] action-value Q for unexpanded nodes

I don't see the AGZ paper explain what the mean action-value Q(s,a) should be for a node that hasn't been expanded yet. The equation for Q(s,a) has the term 1/N(s,a) in it because it's supposed to average over N(s,a) visits. But in this case N(s,a)=0 so that won't work. Does anyone know how this

Re: [Computer-go] action-value Q for unexpanded nodes

ding of the paper, please point to the > specific paragraph that you are having trouble with. > > Álvaro. > > > > On Sun, Dec 3, 2017 at 9:53 AM, Andy <andy.olsen...@gmail.com> wrote: > >> I don't see the AGZ paper explain what the mean action-value Q(s,a) >&g

Re: [Computer-go] action-value Q for unexpanded nodes

Thanks for letting us know the situation Aja. It must be hard for an engineer to not be able to discuss the details of his work! As for the first-play-urgency value, if we indulge in some reading between the lines: It's possible to interpret the paper as saying first-play-urgency is zero. After

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

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

Re: [Computer-go] AlphaGo Zero

I agree with your main point that the first batch of games will be totally random moves. I just wanted to make a small point that even for totally random play, the network should be able to learn something about mid-game positions as well. At move 100, a position with 50 white stones and 40 black

[Computer-go] AGZ Policy Head

Is there some particular reason AGZ uses two 1x1 filters for the policy head instead of one? They could also have allowed more, but I guess that would be expensive? I calculate that the fully connected layer has 2*361*362 weights, where 2 is the number of filters. By comparison the value head

[Computer-go] L011 bots on CGOS are configured wrong

Hoping to contact whoever is running these bots. https://www.reddit.com/r/cbaduk/comments/7nacja/l011_bots_on_cgos_are_configured_wrong/ Copy/paste of my post on reddit: Who is running the L011 bots on CGOS? I'm assuming they are Leela v0.11. They are not configured to remove all dead stones

Re: [Computer-go] Crazy Stone is back

Rémi, can you share any details about how you are training your network? Are you doing self-play games? Do you have access to a large number of GPUs? 2018-02-28 13:04 GMT-06:00 David Wu : > It's not even just liberties and semeai, it's also eyes. Consider for > example

[Computer-go] LZ-1a198f-t1-v2600 on CGOS

-t2 which seems close to optimal on my machine). I have a GTX960, when I run this test I'll post some information on how many playouts it gets in the games. - Andy aka KillerDucky ___ Computer-go mailing list Computer-go@computer-go.org http://computer

Re: [Computer-go] LZ-1a198f-t1-v2600 on CGOS

these but they appear to be recent 10x128 nets, and they use a good amount of time to play so probably don't have a playout/visit cap. I guess I'll wait awhile to see if more LZ bots appear before running any new 10x128 nets myself. - Andy aka KillerDucky 2018-03-04 22:38 GMT-06:00 Andy

[computer-go] Go rating math information

There were some questions about the effective ELO difference of two players 3 ranks apart. Here are some links to information about go rating formulas, and some statistics: http://senseis.xmp.net/?KGSRatingMath

Re: [computer-go] Go rating math information

letters for equivalent constants. So this varying of k is what accounts for the fact that upsets are more likely for weak kyu players than for dan players. - Andy On Jan 31, 2008 12:37 PM, Don Dailey [EMAIL PROTECTED] wrote: ELO ratings don't have to be absolute, just self consistent. So if you

Re: [computer-go] Go rating math information

CrazyStone hasn't played since the initial spike to 1k in December. The movement of the chart afterwards is rating drift. On Jan 31, 2008 12:49 PM, Gian-Carlo Pascutto [EMAIL PROTECTED] wrote: Don Dailey wrote: I don't know how David figures 1000 ELO, but I would expect the difference to

Re: [computer-go] Go rating math information

is improving quickly so there is a strong upward drift. It's usually not so bad for the strong kyus or dans since those players don't generally improve very fast. However as I said in this case CS has only played 10 games, so a much smaller pool of opponents is involved. - Andy On Jan 31, 2008 12

Re: [computer-go] Go rating math information

mcintyre [EMAIL PROTECTED] wrote: From: Andy [EMAIL PROTECTED] Sorry, the KGS formula uses a constant k which is different from the K-factor in Elo. P(A wins) = 1 / ( 1 + exp(k*(RankB-RankA)) ) This would be equivalent to changing the constant 400 in: P(A wins) = 1 / ( 1 + 10^((Ra-Rb)/400

[computer-go] Transformation between KGS ratings and Elo

See below I created a table that shows the transformation from KGS ratings to the Elo that CGOS uses. I set 6k=1800 because I believe that is what GNU 3.7.10 is under both systems. Does anyone have more data points for bots that play on both systems? Also is there an all times list for 19x19?

Re: [computer-go] Re: computer-go Digest, Vol 43, Issue 8

But the program isn't stronger than pros, so how can it give better information about proper komi? On Feb 11, 2008 6:09 PM, Christoph Birk [EMAIL PROTECTED] wrote: On Mon, 11 Feb 2008, Don Dailey wrote: I don't bet, but if I did, I would bet that it's 7 or 8, and I'm fairly certain that

Re: [computer-go] CG'2008 paper: Whole-History Ratings

Remi, you mentioned how the other algorithms predicted well and guessed that it's because the great majority of games are between experienced players whose strength is not changing much. I also feel that the existing KGS ratings work well for those players already. So how about focusing on how

Re: [computer-go] Re: Strength of Monte-Carlo w/ UCT...

On Sun, Aug 10, 2008 at 3:46 PM, Robert Waite [EMAIL PROTECTED]wrote: Okay.. so where is the paper that correlates the speed at which MCwUCT approaches perfect play with the ability to play a human? They seem unrelated as of yet. The closest I've seen are these two studies Don made:

Re: [computer-go] Kaori-Crazystone

I'm excited to see a computer reach 1d as well. For me I'm waiting to see a bot hold a 1d rating consistently on kgs. Right now CrazyStone has been rated 1d briefly, but hasn't been able to maintain it. It's currently 1k. I put a small table of the progress of a few bot's ratings on kgs at

Re: [computer-go] Kaori-Crazystone

On Thu, Sep 4, 2008 at 11:09 AM, Rémi Coulom [EMAIL PROTECTED]wrote: Andy wrote: I'm excited to see a computer reach 1d as well. For me I'm waiting to see a bot hold a 1d rating consistently on kgs. Right now CrazyStone has been rated 1d briefly, but hasn't been able to maintain it. It's

[computer-go] Time controls in bots vs human matches

, and accommodating this doesn't seem like a big deal. Even on CGOS there is a 1s Bronstein delay to prevent silly time loses due to lag. We need the same thing for humans except that for humans it needs to be a bit more than 1s. Same thing for bots on KGS. - Andy

Re: [computer-go] Congratulations to David Fotland and Many Faces

On Mon, Feb 16, 2009 at 7:45 PM, Andy andy.olsen...@gmail.com wrote: See attached a copy of the .sgf. It was played private on KGS so you can't get it there directly. One of the admins cloned it and I saved it off locally. I changed the result to be B+4.5 instead of W+2.5. I forgot to make

Re: [computer-go] The Zen program

Yamato, It looks like Zen19 doesn't implement handicap stone komi compensation the same way kgs does for Chinese rules. It's the only reason I can think that it lost this game: http://files.gokgs.com/games/2009/3/27/TaPaHka-Zen19.sgf - Andy, aka yoyoma On Thu, Mar 26, 2009 at 5:54 PM, Yamato

[computer-go] Extra komi for Handicap stone compensation on KGS

FYI, here is a link and the relevant quotes for the way KGS gives white extra komi based on handicap stones in some rules. http://www.gokgs.com/help/rulesets.html Chinese - The white player is given one point extra komi for every handicap stone that Black gets at the start of the game. AGA -

Re: [computer-go] Big trees

Put it on cgos and see how good it is! On Fri, Jul 10, 2009 at 9:10 PM, Michael Williams michaelwilliam...@gmail.com wrote: Now that I have this system of generating really big game trees, what sort of interesting things could I do with it? The exact number of nodes I can store is not exact