On Wed, Dec 19, 2007 at 12:21:18AM -0500, Chris Fant wrote:
I just witnessed CrazyStone defend a fairly long ladder, resulting in
a dead 17-stone block. Why not use a ladder reader at the root of the
UCT tree to prevent provably bad ladder moves from being considered?
I don't know for sure,
On Dec 19, 2007 9:40 AM, Heikki Levanto [EMAIL PROTECTED] wrote:
On Wed, Dec 19, 2007 at 12:21:18AM -0500, Chris Fant wrote:
I just witnessed CrazyStone defend a fairly long ladder, resulting in
a dead 17-stone block. Why not use a ladder reader at the root of the
UCT tree to prevent
On Dec 11, 2007 11:36 AM, Rémi Coulom [EMAIL PROTECTED] wrote:
Question: how do MC programs perform with a long ladder on the board?
Crazy Stone handles ladder with progressive widening. Ladder atari is
usually ranked first or very high in the move list, and ladder extension
lower. So, the
I just witnessed CrazyStone defend a fairly long ladder, resulting in
a dead 17-stone block. Why not use a ladder reader at the root of the
UCT tree to prevent provably bad ladder moves from being considered?
I meant to include the CGOS-19 game number: 7613
The game is still in progress as
Forrest, similar multi-level or hierarchical/partitioned search concepts
have been suggested by several people here over the years, myself
included many times. I first suggested a chunking probability based
search concept back in 1998.
I have long been an advocate of goal-directed
On Dec 13, 2007 2:03 AM, Harald Korneliussen [EMAIL PROTECTED] wrote:
Wed, 12 Dec 2007 07:14:48 -0800 (PST) terry mcintyre wrote:
Heading back to the central idea, of tuning the predicted winning
rates and evaluations: it might be useful to examine lost games, look
for divergence between
steve uurtamo wrote:
Currently there is no evidence whatsoever that probability estimates
are
inferior and they are the ones playing the best GO right now
are they?
Yes - in both 9x9 and 19x19 go.
- Don
s.
Currently there is no evidence whatsoever that probability estimates
are
inferior and they are the ones playing the best GO right now
are they?
s.
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On 12/11/07, Mark Boon [EMAIL PROTECTED] wrote:
Question: how do MC programs perform with a long ladder on the board?
My understandig of MC is limited but thinking about it, a crucial
long ladder would automatically make the chances of any playout
winning 50-50, regardless of the actual
Eric,
Yes, as Magnus also stated MC play-out doesn't really accurately
estimate the real winning probability but it still get the move order
right most of the time.
The situation is that if the position is really a win, it doesn't mean
that a MC is able to find the proof tree. But it
Jason House wrote:
MoGo uses TD to predict win rates.
Really? Where did you get that information?
--
GCP
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On Dec 13, 2007 11:39 AM, Gian-Carlo Pascutto [EMAIL PROTECTED] wrote:
Jason House wrote:
MoGo uses TD to predict win rates.
Really? Where did you get that information?
I can't seem to load http://www.lri.fr/~gelly/MoGo.htm at the moment, but I
found it there. One of the papers you can
It's the approach I believe to be more human-like. Not necessarily the
playing style.
Human beings chunk.
What all this fuss suggests to me is a meta-mc program... You
include routines that work out good sequences, as a human would--and
then you have the random part of the program
On Dec 13, 2007 2:28 PM, Forrest Curo [EMAIL PROTECTED] wrote:
It's the approach I believe to be more human-like. Not necessarily the
playing style.
Human beings chunk.
What all this fuss suggests to me is a meta-mc program... You
include routines that work out good sequences, as a
Jason House wrote:
The paper introduces RAVE and
near the end talks about using heuristics for initial parameter
estimation. The heuristic they used was based TD.
Ah, you're talking about RLGO. RLGO was trained with TD, but MoGo itself
doesn't use TD (directly).
There are posts from Sylvain
On Dec 13, 2007 3:52 PM, Gian-Carlo Pascutto [EMAIL PROTECTED] wrote:
Jason House wrote:
The paper introduces RAVE and
near the end talks about using heuristics for initial parameter
estimation. The heuristic they used was based TD.
Ah, you're talking about RLGO. RLGO was trained with
Quoting Álvaro Begué [EMAIL PROTECTED]:
On Dec 13, 2007 2:28 PM, Forrest Curo [EMAIL PROTECTED] wrote:
It's the approach I believe to be more human-like. Not necessarily the
playing style.
Human beings chunk.
What all this fuss suggests to me is a meta-mc program... You
include routines
On Tue, 2007-12-11 at 21:17 -0500, Don Dailey wrote:
But what does this have to do with anything? What we are arguing
about is whether it's good to try to estimate probabilities. That's
what you have been critical of. Adding ladder code will improve any
evaluation function if done
Raymond Wold wrote:
I can code an algorithm that evaluates simple ladders correctly.
I'll repeat that. I can code a program that reads ladders better than a
pure MC program without knowledge of ladders. I can beat it. Human
knowledge programmed into a computer that does that one thing, that
David
-Original Message-
From: [EMAIL PROTECTED] [mailto:computer-go-
[EMAIL PROTECTED] On Behalf Of Don Dailey
Sent: Tuesday, December 11, 2007 11:53 AM
To: computer-go
Subject: Re: [computer-go] How does MC do with ladders?
Hi Petri,
I happen to think that MC is the most
Russ Williams wrote:
On Dec 11, 2007 8:53 PM, Don Dailey [EMAIL PROTECTED] wrote:
The play-out portion is a crude approximation for imagination. We
basically look at a board and imagine the final position.The MC
play-outs kill the dead groups in a reasonably accurate (but fuzzy)
Why does anybody care about how human-like our go programs' playing style
is? When we design airplanes we don't care about how bird-like their flying
style is; we care about objective measures like speed, acceleration, energy
efficiency... The merits of go programs should be based basically on
Álvaro Begué wrote:
Why does anybody care about how human-like our go programs' playing
style is? When we design airplanes we don't care about how bird-like
their flying style is; we care about objective measures like speed,
acceleration, energy efficiency... The merits of go programs should
to be masters. -- Daniel Webster
- Original Message
From: Raymond Wold [EMAIL PROTECTED]
To: computer-go computer-go@computer-go.org
Sent: Wednesday, December 12, 2007 12:23:15 AM
Subject: Re: [computer-go] How does MC do with ladders?
On Tue, 2007-12-11 at 21:17 -0500, Don Dailey wrote
; but they mean to be masters. -- Daniel Webster
- Original Message
From: Raymond Wold [EMAIL PROTECTED]
To: computer-go computer-go@computer-go.org
Sent: Wednesday, December 12, 2007 12:23:15 AM
Subject: Re: [computer-go] How does MC do with ladders?
On Tue, 2007-12-11 at 21:17 -0500, Don
Wed, 12 Dec 2007 07:14:48 -0800 (PST) terry mcintyre wrote:
Heading back to the central idea, of tuning the predicted winning
rates and evaluations: it might be useful to examine lost games, look
for divergence between expectations and reality, repair the predictor,
and test the new predictor
Mark Boon wrote:
Question: how do MC programs perform with a long ladder on the board?
Mogo makes the 20k mistake to push an intrusion of ladder shape into the
own territory like tooth paste. I do not know if this is caused by
reading ladder-like, by juding the adjacent life wrongly (in a
Since Valkyria is slow anyway, I can have it read ladders in the
simulations. The ladder code is really fast and a little buggy, but
works often enough to not cause major problems. I never tested the
benefits of the ladder code it just appeared to be much stronger.
-Magnus
Quoting Rémi
Rémi Coulom wrote:
I don't understand what you mean by push an intrusion of ladder shape
into the own territory like tooth paste.
The game below is a 9 stone handicap game between me and Mogo. It is my
second game against Mogo, after a 7x7 test to understand the GUI and a
first even game
Ladders are not hard, especially if one is permitted to place stones on the
(virtual) board to trace the flow. A 20 kyu human can follow the logic.
Don, you describe some subtle choices of playing one's opponent, and compare
them to MC programs, but you are a fairly strong chess player. If you
Raymond Wold wrote:
On Tue, 2007-12-11 at 11:42 -0500, Don Dailey wrote:
In fact, this illustrates a wonderful strength of these programs.
Only it's not strength to ignore a move to your benefit,
Who suggested that it was? The strength of MC programs is how they
deal with
terry mcintyre wrote:
Ladders are not hard, especially if one is permitted to place stones
on the (virtual) board to trace the flow. A 20 kyu human can follow
the logic.
Don, you describe some subtle choices of playing one's opponent, and
compare them to MC programs, but you are a fairly
At this point, it has to be said that _all_ computer go programs suck at 19xc19
go. MC programs happen to suck less, especially on small boards.
On the other hand, we do have some very strong special-purpose go programs.
There are several very strong tsumego/life-and-death programs and at least
Hi Petri,
I happen to think that MC is the most human like approach currently
being tried.
The reason I say that is that humans DO estimate their winning chances
and tally methods, where you simply tally up features/weights
(regardless of how sophisticated) is not how strong humans think
2007/12/11, terry mcintyre [EMAIL PROTECTED]:
With Go, there are many situations which can be read out precisely, provided
that one has the proper tools - ladders, the ability to distinguish between
one and two eyes; the ability to reduce eyespaces to a single eye with an
appropriate
Make sure that you use the -19 argument when starting 19x19 Mogo, and
restart GoGui (in order to restart Mogo indirectly) after you change
the settings. Somewhat confusingly, Mogo does not automatically play
19x19 style just because it receives a request for 19x19 board. Poor
ladder handling and
On Tue, 2007-12-11 at 13:45 -0500, Don Dailey wrote:
Do you know of an approach that evaluates go positions perfectly?You
are attacking the fact that MC programs have errors in their probability
estimates but completely ignoring the fact that SO DOES EVERY OTHER
EVALUATION FUNCTION.
I
2007/12/12, Raymond Wold [EMAIL PROTECTED]:
Are you saying that there is absolutely no way to combine such with an
MC program to make it better? Not just that no one has done it (I don't
know if anyone has) but that it is impossible? Are you saying that
attempts to do so are wasted? If you
Since Valkyria is slow anyway, I can have it read ladders in the
simulations. The ladder code is really fast and a little buggy, but
works often enough to not cause major problems. I never tested the
benefits of the ladder code it just appeared to be much stronger.
-Magnus
What do you do
Raymond,
Playing a strong game of go is a combination of many factors, not just
reading ladders.You could probably isolate out any particular skill
and write some code that does it pretty well. But the question will
always be: How well does it actually play the game?
As has been stated
I have had this experience many times:
1. You see a move that sucks.
2. You identify the problem and engineer a solution.
3. The solution indeed works - it cures the problem.
4. The program plays worse than it did before.
By the way, you are being modest, Antigo is not bad on
On Dec 11, 2007 8:53 PM, Don Dailey [EMAIL PROTECTED] wrote:
The play-out portion is a crude approximation for imagination. We
basically look at a board and imagine the final position.The MC
play-outs kill the dead groups in a reasonably accurate (but fuzzy) way
and put the flesh on the
2007/12/11, Don Dailey [EMAIL PROTECTED]:
Hi Petri,
I happen to think that MC is the most human like approach currently
being tried
Ye in sense Alpha-Beta is human like. It one feature we do and takes
it to extreme. And using different method of evaluation.
.
The reason I say that is that
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