o-boun...@computer-go.org] On Behalf Of
> Ray Tayek
> Sent: Friday, May 05, 2017 10:44 PM
> To: computer-go@computer-go.org
> Subject: Re: [Computer-go] software like: http://ps.waltheri.net/
>
> On 5/5/2017 5:38 PM, David Fotland wrote:
> > Many Faces of Go Fuseki tutor can
Many Faces of Go Fuseki tutor can do this, but I'd have to help if you want to
start from an empty database. That's how I generate the tutor. You can add sgf
files to the existing tutor pretty easily.
David
> -Original Message-
> From: Computer-go
I think the character set property just refers to the contents of comments and
similar fields. The sgf format itself is entirely in the common characters in
UTF-8 and US-ASCII. There is no need to assume a character set before the
property. If you find the character set property in the root
Alpha's publication is pretty clear on how they did it. Now that their research
has shown the way, other competent teams with similar compute resources should
be able to duplicate their work. It has been almost a year, which is enough
time.
David
> -Original Message-
> From:
Because you test it both ways, and one wins more games. Many things about the
playout policy are mysterious and can only be tested to see if they make play
stronger. Often the results of testing are counterintuitive. I'd guess only
about a quarter of the things I tried in Many Faces made the
Remi has something: https://www.remi-coulom.fr/kifu-snap/
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
> Hideki Kato
> Sent: Thursday, November 24, 2016 4:17 PM
> To: computer-go@computer-go.org
> Subject: [Computer-go] Auto Go game
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
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
> Hiroshi Yamashita
Amazon p2.16xlarge instance gets you 64 cores (Xeon E5-2686v4) and 16 K80 GPUs
for $14.40 per hour. Not bad if you just want to run it during a competition.
David
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
> Cameron Browne
> Sent:
Many faces does most of what has been mentioned. In addition, rather than stop
search when it is impossible for another move to be chosen, I stop earlier,
when it is unlikely for another move to become best. When far ahead, I stop a
little earlier. That preserves some time in case there is a
I train using approximately the same training set as AlphaGo, but so far
without the augmentation with rotations and reflection. My target is about
55.5%, since that's what Alphago got on their training set without
reinforcement learning.
I find I need 5x5 in the first layer, at least 12
https://www.reddit.com/r/pokemongo/comments/4tez82/how_pokemon_really_play_go/
Although it looks like they are actually playing Go Moku.
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go
Correction on ManyFaces hardware. Running on a 4-core i7-4790 3.6 GHz, without
a GPU, using a deep neural net (that I trained on KGS games).
David
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Nick Wedd
Sent: Saturday, July 16, 2016 8:21 AM
To:
I don’t expect AlphaGo will be available at any price, but I expect similar
strength programs will be running on high end PCs in a few years. The AlphaGo
team has done an outstanding job of exploring the solution space and showing us
the way. Others can now tweak and optimize and find more
The alphaGo network is detailed in their paper. They have about 50 binary
inputs, one layer of 5x5 convolutional filters, and about 12 layers of 3x3
convolutional filters. Detlef’s net is specified in the prototxt file he
published here. It’s wider and deeper, but with fewer inputs.
The
training
You can also use hdf5 format, which has transparent compression as well as
coffee support.
Josef
Dne st 27. 4. 2016 18:06 uživatel Gian-Carlo Pascutto <g...@sjeng.org> napsal:
On 27-04-16 17:45, David Fotland wrote:
> I’d rather just buy another drive than spend ti
a factor of 32 compression right there, and you might be using
constant planes for some inputs, and if the output is a move it fits in 9
bits...
Álvaro.
On Wed, Apr 27, 2016 at 12:55 AM, David Fotland <fotl...@smart-games.com> wrote:
I have my deep neural net training setup w
I have my deep neural net training setup working, and it's working so well I
want to share. I already had Caffe running on my desktop machine (4 core
i7) without a GPU, with inputs similar to AlphaGo generated by Many Faces
into an LMDB database. I trained a few small nets for a day each to get
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
> Darren Cook
> Sent: Wednesday, March 23, 2016 5:19 AM
> To: computer-go@computer-go.org
> Subject: *SPAM* Re: [Computer-go] UEC cup 2nd day
>
> David Fotland
I have sgf’s of the Many Faces’ games, but I finished 8th. I don’t have the
top games.
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Pawel Morawiecki
Sent: Sunday, March 20, 2016 1:57 AM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] UEC cup 2nd day
There are 12 programs here that have deep neural nets. 2 were not qualified
for the second day, and six of them made the final 8. Many Faces has very
basic DNN support, but it’s turned off because it isn’t making the program
stronger yet. Only Dolburam and Many Faces don’t have DNN in the
Smart-games.com is getting a big increase in traffic, so there is certainly
more interest in the game now. I hope it holds up for the long term.
David
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Dmitry Kamenetsky
Sent: Saturday, March 12, 2016 2:18 PM
To:
Tremendous games by AlphaGo. Congratulations!
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Lukas van de Wiel
Sent: Saturday, March 12, 2016 12:14 AM
To: computer-go@computer-go.org
Subject: [Computer-go] Congratulations to AlphaGo
Whoa, what a fight! Well
He was already in Byo-yomi, so perhaps he didn’t have an accurate count. This
might explain why he looked upset at move 175. He might have realized his
mistake.
David
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf
> Of Darren Cook
>
Yes, I think the programs will have similar biases. In this game Sedol had
some groups that were alive, but needed correct responses to stay alive. Even
though the pro's stones won’t die, the playouts sometimes manage to kill them.
This makes the program think it is more ahead than it
I predicted Sedol would be shocked. I'm still routing for Sedol. From
Scientific American interview...
Schaeffer and Fotland still predict Sedol will win the match. “I think the pro
will win,” Fotland says, “But I think the pro will be shocked at how strong the
program is.”
>
> P.S. Lee
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
gt; * Desktop: CPU i7-4770 (Haswell), 3.5 GHz , DRAM - 16 GB; GPU K20.
> >>> * Ubuntu 12.04; gcc 4.7.3; MKL 11.1.
> >>>
> >>> Test:: imagenet, 100 train iteration (batch = 256).
> >>>
> >>> * GPU: time= 260 sec / memory = 0.8 GB
I got the basics of Machine learning (including sample neural nets) from Andrew
Ng's course course, two or three years ago. I highly recommend it. Lots of
practical advice. The rest came from reading papers and probably some on-line
searches. Amazon's Computer Vision team uses deep neural
t; Also, even quite big nets probably can be run on modest GPUs reasonably
> well (within memory bounds). It's the training where the size really
> hurts.
>
> On Tue, Mar 1, 2016 at 6:19 PM, Petr Baudis <pa...@ucw.cz> wrote:
> > On Tue, Mar 01, 2016 at 09:14:39AM -0800, Da
Very interesting, but it should also mention Aya.
I'm working on this as well, but I haven’t bought any hardware yet. My goal is
not to get 7 dan on expensive hardware, but to get as much strength as I can on
standard PC hardware. I'll be looking at much smaller nets, that don’t need a
GPU
] Move evalution by expected
> value, as product of expected winrate and expected points?
>
> My 1.5 cent:
>
> David Fotland has a nice score-estimator in his (old) ManyFaces bot.
> The score estimator is still from the days before the Monte Carlo
> version.
>
> Perhaps
gt; Hi David,
>
> I am not happy with my IDE on linux too. You might give Visual Studio on
> linux a try:
>
> https://www.visualstudio.com/de-de/products/code-vs.aspx
>
> It seems to be free...
>
> Detlef
>
> Am 05.02.2016 um 07:13 schrieb David Fotland:
> &
I'm not using it. Many Faces is written in c, (gui in C++ with MFC). I ported
caffe to windows and I'm calling caffelib directly from mfgo. I'm not training
a net yet, so I haven’t decided what to do. Most likely I will create the
input database using c++ code in many faces, and train using
access to
halting your machine if you are deep in the guts. ;)
s.
On Tue, Feb 2, 2016 at 10:25 AM, David Fotland <fotl...@smart-games.com> wrote:
Detlef, Hiroshi, Hideki, and others,
I have caffelib integrated with Many Faces so I can evaluate a DNN. Thank you
very much Detle
Robert, please consider some of this as the difference between math and
engineering. Math desires rigor. Engineering desires working solutions. When
an engineering solution is being described, you shouldn't expect the same level
of rigor as in a mathematical proof. Often all we can say is
Amazon uses deep neural nets in many, many areas. There is some overlap with
the kind of nets used in AlphaGo. I passed a link to the paper on to one of
our researchers and he found it very interesting. DNN works very well when
there is a lot of labelled data to learn from. It can be useful
Detlef, Hiroshi, Hideki, and others,
I have caffelib integrated with Many Faces so I can evaluate a DNN. Thank you
very much Detlef for sample code to set up the input layer. Building caffe on
windows is painful. If anyone else is doing it and gets stuck I might be able
to help.
What
buntu update updated the graphics driver: I had 2
> times in the last year to reinstall cuda (a little ugly, as the graphic
> driver did not work after the update and you had to boot into command
> line mode).
>
> Detlef
>
> Am 02.02.2016 um 19:25 schrieb David Fotland:
> > Detlef
Google’s breakthrough is just as impactful as the invention of MCTS.
Congratulations to the team. It’s a huge leap for computer go, but more
importantly it shows that DNN can be applied to many other difficult problems.
I just added an answer. I don’t think anyone will try to exactly
e playing
> > strength of the old program and get a fair comparison it should again
> > run on an old machine while the modern go-programs use today's hardware.
> > - Michael.
>
> I discussed your point in depth with David Fotland (father of MFoG).
> Back in 1998, MFoG had
1 kyu on KGS with no search is pretty impressive. Perhaps Darkforest2 is too
slow.
David
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Andy
Sent: Monday, November 23, 2015 9:48 AM
To: computer-go
Subject: Re: [Computer-go] Facebook Go AI
As of about an
The non-mcts levels of Many Faces try to maximize score, with some bias toward
safety when ahead. The non-MCTS version uses dynamic komi to avoid giving up
points in the endgame, but this is not in the version 12 released engine.
David
From: Computer-go
Attempting to maximize the score is not compatible with being a strong engine.
If you want a dan level engine it is maximizing win-probability.
David
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
> Darren Cook
> Sent: Tuesday,
Yu Bin won his game against Dolbaram. The second official pro game is
happening now. 51wq.lianzhong.com/yidongwq
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
fotl...@smart-games.com
Sent: Saturday, November 14, 2015 1:35 AM
To: computer-go@computer-go.org
Many Faces of Go doesn’t use Remi’s playout policy and I don’t think Zen does
either. I don’t think Remi’s and Mogo’s are similar either, since they were in
some ways competing developments. The bias issue is very real, so as you add
knowledge to the playouts you have to be careful to add
Many Faces of Go has 2052 3x3 patterns. All have a empty point in the center.
One value is used for all the illegal patterns, so there are 2051 valid
patterns. I use Aja’s idea of including in the pattern the Atari status of
zero to four adjacent groups. That’s why it’s more than Álvaro’s
I've been helping them test their client and there are some issues. They
are working on fixing them. If you are having problems while testing,
please email me directly at fotl...@smart-games.com for details, and I can
save you some time or provide worarounds. I don't have email addresses for
Many Faces only has big nodes with all of the child statistics in one node,
along with the totals for the position. Like the right hand of your diagram,
but also with the 11/22 totals. There is no tree. All nodes are in a big
transposition table and there are no child or parent pointers. I
Many Faces uses 2200 for RAVE_EQUIV. I found that anything between 2000 and
3000 was about the same, and CLOP recommended 2200. 1000 was a little worse,
and 500 was much worse. In discussions with other programmers I heard numbers
between and 5000.
For parameter tuning I recommend
-Original Message-
> From: Computer-go [ <mailto:computer-go-boun...@computer-go.org>
> mailto:computer-go-boun...@computer-go.org] On Behalf
> Of David Fotland
> Sent: 15 October 2015 06:51
> To: <mailto:computer-go@computer-go.org> computer-go@computer
In 2008 Many Faces was getting about 25k light playouts per second on 19x19.
Today it gets 2500 playouts per second on one thread of an i7-3770. I don’t
use a probability distribution in the UCT tree. I both count liberties and
maintain lists of liberty points, but all incrementally. In the
There is an easy way to enforce computational limits. Ask everyone to run on
an identical AWS instance. Nevertheless, I’m against identical hardware
tournaments except as a special rare exception.
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
David Doshay
You could what they do in bridge tournaments, and provide two sets of results
from the same tournament.
Hardware would be unrestricted for everyone
The Open result would include all participants, exactly as today.
A "single machine" result would only include participants that ran on a single
I don’t share or take code from other programs because Many Faces of Go is
commercial. Many other programs have licenses that are not compatible with
commercial use, so I'm careful not to even look at their source code. We share
ideas all the time, through publications, informal conversations
I never tried to optimize stopping, so my stopping rule is very conservative.
Many Faces stops at twice the number of points on the board, or if the mercy
rule triggers. The mercy rule requires one side to have many more stones on
the board than the other (at least 1/3 of the number of points
Yes, in the old engine, I roll everything up into a single number, with a
resolution of 1/100th of a point (only so the total score would fit in a 16 bit
integer on the 16 bit machine I used for development in 1982).
I would say rather, that expert systems are dead in Go because many smart
No, simple radiation is not the best, although some programs (including mine)
started with something like this. I think the best approach was Reiss' Go4++,
where territory was modelled using connectivity. If a new stone can be
connected to a living group of the same color, then this point
I agree that group strength can't be a single number. That's why I classify
groups instead. Each classification is treated differently when estimating
territory, when generating candidate moves, etc. The territory counts depend
on the strength of the nearby groups.
Monte Carlo has a big
ber 04, 2015 12:34 AM
> To: computer-go@computer-go.org
> Subject: Re: [Computer-go] re comments on Life and Death
>
> On 04.09.2015 07:25, David Fotland wrote:
> > group strength and connection information
>
> For this to work, group strength and connection status must
I forgot, I did publish a paper on Many Faces:
https://www.researchgate.net/publication/220174515_Static_Eye_Analysis_in_The_Many_Faces_of_Go
I'm not sure it's available online.
David
> -Original Message-
> From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
> Behalf Of
Many Faces of Go is MC + expert system (plus local search, etc). The reason I
won the world championship in 2008 is because I implemented MCTS but
incorporated the old Many Faces expert system move generator and ranking. This
is pretty slow (a few hundred positions a second), so when the tree
No. Since MF's search is so highly pruned, and directly by the expert system
move generator, it scales poorly with computer power. If I went back to the
pure MFGO engine and added the modern ELO based pattern from Remi's approach, I
think it would be a couple of stones stronger, but still
Probably everyone does something different for Dynamic komi. There have a few
publications.
In MFGO, the shipping version 12 doesn’t use dynamic komi, but the KGS version
has it, and it's probably worth about half a stone. My algorithm tries to keep
the win rate between 55% and 60% when
Many Faces of Go gives reasons for its moves after fact. It reasons about the
position using go proverbs, life and death analysis, group strength and
connection information, etc. If you have a copy, you can ask it to explain its
reasons for making a move. There were far more than a few
Congratulations to Aya. The commentary on the ManyFaces vs Aya game is very
interesting.
David
-Original Message-
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
Behalf Of Petr Baudis
Sent: Wednesday, July 29, 2015 2:21 PM
To: computer-go@computer-go.org
In general this is beyond the state of the art of the strongest go programs.
You can’t score without determining the status of every group (live, dead,
seki), and you may need to identify required interior defensive moves that have
not been played.
David
From: Computer-go
I can't travel to Europe for this tournament. The main issue for me is
arranging a local operator. I have no way to do that.
Regards,
David
-Original Message-
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
Behalf Of Petr Baudis
Sent: Sunday, July 05, 2015 11:37
Converting back and forth from eval to winning probability is interesting, as
is combining the quick win threat and long term advantage evals.
David
-Original Message-
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
Behalf Of Darren Cook
Sent: Wednesday, April 22,
I didn’t notice a difference. Like everyone else, once I had RAVE implemented
and added biases to the tree move selection, I found the UCT term made the
program weaker, so I removed it.
David
-Original Message-
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On
For many faces, moves like, any Atari, fill a liberty in a losing semeai,
attack a group that is alive but doesn’t have two clear eyes yet.
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Stefan Kaitschick
Sent: Saturday, January 10, 2015 1:13 AM
To:
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? If my engine uses a GPU or is a multinode cluster, how does
that run on your docker service?
David
From:
Why don’t you make a dataset of the raw board positions, along with code to
convert to Clark and Storkey planes? The data will be smaller, people can
verify against Clark and Storkey, and they have the data to make their own
choices about preprocessing for network inputs.
David
You can do some GPU experiments on Amazon AWS before you buy. 65 cents per hour
David
http://aws.amazon.com/ec2/instance-types/
G2
This family includes G2 instances intended for graphics and general purpose GPU
compute applications.
Features:
High Frequency Intel Xeon E5-2670 (Sandy Bridge)
This would be very similar to the integration I do in Many Faces of Go. The
old engine provides a bias to move selection in the tree, but the old engine is
single threaded and only does a few hundred evaluations per second. I
typically get between 40 and 200 playouts through a node before Old
Is this 23 cores SMP working on the same tree, or four by 6-cores? I'm
running a cluster of four 4-core 2.3 GHz machines, using MPI to share the
core of the trees a few times a second.
The results between zen-1c, mfgo-16c and pachi-23c are interesting.
Zen wins about 60% against many
To: computer-go
Subject: Re: [computer-go] Strong programs on cgos 19x19?
or the strong version of pachi.
Done.
Jean-loup
2010/2/16 David Fotland fotl...@smart-games.com
My old MPI code had a scaling bug. Performance scaling (playouts per
second) was linear, but the strength did
My old MPI code had a scaling bug. Performance scaling (playouts per
second) was linear, but the strength did not scale well, and 64 cores was
weaker than 32 cores. I have a 16 core cluster of my own now (four 2.3 GHz
Q8200 quad core), and I discovered that the MPI code hangs when using MPICH2
Many Faces has the same issue. The pruning and tuning that is required for
19x19 doesn't help 9x9. It seems that now the programs are strong enough
that 9x9 requires a good opening book, and I'd rather spend my time making
19x19 stronger.
David
Zen's algorithm is getting heavier and
Many Faces does almost the same thing (handicap games with black only, 7
points per handicap stone, decreasing linearly with move number to move 90).
It looks like this change gained about half a rank on KGS.
David
-Original Message-
From: computer-go-boun...@computer-go.org
I think you can only evaluate static evaluation in the context of a search
and a tournament between programs. You could start with a simple 1-ply
search and play against gnugo. Strength in life and death or predicting pro
moves doesn't correlate with the ability to win games.
David
: [computer-go] 13x13 human vs computer
9*9: 6 dan
19*19 :1 kyu
13*13 1 dan?
not the expected interpolation :-)
Looks like programing for a specific board size is important.
Stefan
- Original Message -
From: David Fotland fotl...@smart-games.com
To: 'computer-go
On 13x13, Many Faces is probably 1 Dan on KGS. I dont know how that
translates to German ranks, but probably 2 stones is fair, or 3 stones if
you want the computer to likely win.
David
-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
You say radius = 3, then 3x3 patterns. Which is it? Radius 3 would be 5x5
to 7x7, depending on how you define the radius.
David
-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Petr Baudis
Sent: Saturday,
When I've looked at these losses they were due either to bugs, or to bias in
the playouts, for example when there is a semeai. The program will think it
has 80% win rate when it is actually already behind.
David
-Original Message-
From: computer-go-boun...@computer-go.org
This is what I do (no tree, just a hash table). The cost is that the nodes
become very large because every node also holds all the child information,
all rave counters, etc. So memory usage is higher.
David
From: computer-go-boun...@computer-go.org
Many Faces keeps the tree from move to move. I discard nodes with few visits
(or old nodes) after each move to free up most of the tree memory, but I keep
the core of the tree. When MF runs out of memory it garbage collects some
nodes.
David
From: computer-go-boun...@computer-go.org
I use two values. I never even occurred to me to use three.
David
-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Petr Baudis
Sent: Tuesday, December 08, 2009 2:50 PM
To: computer-go@computer-go.org
Subject:
Windows builds for GNU Go and Fuego : http://gnugo.baduk.org/
Fuego opening books : http://gnugo.baduk.org/fuegoob.htm
On Sat, Dec 5, 2009 at 5:54 AM, David Fotland fotl...@smart-games.com
wrote:
Many Faces is getting too strong for Gnugo. I test using 8K playouts per
move on 19x19 and win about
: Saturday, December 05, 2009 2:40 AM
To: computer-go
Subject: Re: [computer-go] Fuego parameter question
On Fri, Dec 04, 2009 at 08:54:39PM -0800, David Fotland wrote:
Many Faces is getting too strong for Gnugo. I test using 8K playouts
per
move on 19x19 and win about 89% of the games
Thanks. I tried giving both fuego and MFGO 16K playouts and stopped at with
MFGO winning 123/135 = 91% +- 4%. I'm starting again with your suggestions:
uct_param_player ignore_clock 1
uct_param_player max_games 16000
uct_param_search number_threads 1
uct_param_player ponder 0
go_param_rules
Many Faces is getting too strong for Gnugo. I test using 8K playouts per
move on 19x19 and win about 89% of the games.
I just tried testing against Fuego to get a stronger opponent. I used
fuego-svn985 from http://gnugo.baduk.org/, already built for Windows.
I ran it with:
fuego
I watched the pro matches. It looked like a 4 dan beat Zen with 6 stones,
and a 9 dan beat KCC with 6 stones.
David
-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Ian Osgood
Sent: Sunday, November 29, 2009 9:16
I think it was a single elimination, not a swiss tournament, and I think
many of the strong programs were in the same bracket. I think Many Faces
lost to KCC in an early round and wasn't paired against the other strong
programs. We'll have to wait for the full results to check.
David
of program strength.
Regards,
David Fotland
-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of
Sent: Sunday, November 29, 2009 5:08 PM
To: computer-go
Subject: Re: [SPAM] Re: [computer-go] Live broadcasting
-boun...@computer-go.org
[mailto:computer-go-boun...@computer-go.org] On Behalf Of ? ?
Sent: Monday, November 09, 2009 7:54 PM
To: computer-go
Subject: Re: [computer-go] Joseki Book
From what David Fotland has said, Many Faces will lay out whole josekis as
single moves in its searches, which
Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Robert Jasiek
Sent: Monday, November 09, 2009 9:38 PM
To: computer-go
Subject: Re: [computer-go] Joseki Book
David Fotland wrote:
in a two play global search, an entire joseki
Knowpap.txt is how The Many Faces of Go represents knowledge. Smart Go is a
different program.
Until a few years ago the strongest programs all used knowledge-intensive
approaches with highly pruned local searches, like Many Faces.
Now the strong programs all use Monte Carlo Tree Search,
I'm not sure what you are asking, but when you are playing with Japanese
rules, don't pass in the middle game. The simple solution is to wait until
the game is over and all dame are filled before you pass.
From: computer-go-boun...@computer-go.org
[mailto:computer-go-boun...@computer-go.org]
I share all uct-nodes with more than N visits, where N is currently 100, but
performance doesn't seem very sensitive to N.
Does Mogo share RAVE values as well over MPI?
I agree that low scaling is a problem, and I don't understand why.
It might be the MFGO bias. With low numbers of playouts
In the MPI runs we use an 8-core node, so the playouts per node are higher.
I don't ponder, since the program isn't scaling anyway.
The number of nodes with high visits is smaller, and I only send nodes that
changed since the last send.
I do progressive unpruning, so most children have zero
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