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
steve uurtamo wrote:
i think that maybe you misunderstand how byo yomi is used in
practice.
you have a giant pile of time that should be enough to account for
basically all of the hardest parts of the game.
then you have several (more than 1 !) byo-yomi periods, which are
like grace periods on
Don Dailey wrote:
On Sun, 2007-04-08 at 11:24 +0200, Heikki Levanto wrote:
In fact this is how beginners think about the game. It doesn't
seem to me like a good learning aid to try to get the computers
to emulate the losing strategy weaker players use.
Weaker players can not estimate the
Don Dailey wrote:
(snip)
In my opinion, the insight that Chrilly articulated was that all of
sudden we are now all using some type of global search - the very
idea was considered blasphemy just 2 or 3 years ago.
That may be too strong a statement. It may have not been popular but
many people
Erik van der Werf wrote:
On 4/10/07, alain Baeckeroot [EMAIL PROTECTED] wrote:
Le lundi 9 avril 2007 14:06, Don Dailey a écrit:
But the point is that
as long as you can provide time and memory you will get improvement
until perfect play is reached.
Is there any proof that heavy player
Don Dailey wrote:
On Mon, 2007-03-05 at 10:10 +0100, Heikki Levanto wrote:
On Sat, Mar 03, 2007 at 04:10:16PM -0500, Don Dailey wrote:
And you CAN compare GTP directly to UCI because both are designed for
the same purpose and both are simple text based protocols and the
similarities are much
Heikki Levanto wrote:
On Wed, Feb 21, 2007 at 07:55:15PM -0600, Matt Gokey wrote:
Whether it is a torus or not is irrelevant. The only thing that matters
from a go game play perspective is the graph topology. If all points
have 4 neighbors the actual physical shape or layout doesn't matter
alain Baeckeroot wrote:
Le jeudi 22 février 2007 14:11, Matt Gokey a écrit :
The only thing that matters is the graph topology. A corollary is
that on any board that is completely balanced at the beginning with
identical number of neighbors for all nodes, any 1st play is
equivalent
Tapani Raiko wrote:
Matt Gokey wrote:
I'm not sure I agree with this. I hypothesize that 2d, 3d, 4d, torus,
or any other shape is completely irrelevant with regard to game play.
The only thing that matters is the graph topology. A corollary is that
on any board that is completely balanced
Nick Apperson wrote:
I considered making a version of go that plays with tetrahedral
geometry. It is a 3D arrangment where all nodes have 4 neighbors and
the angles between each are 109 degrees. Its connection properties
though are very different because of the way it it layed out.
Hence, I
Matt Gokey wrote:
alain Baeckeroot wrote:
Le jeudi 22 février 2007 14:11, Matt Gokey a écrit :
The only thing that matters is the graph topology. A corollary is
that on any board that is completely balanced at the beginning with
identical number of neighbors for all nodes, any 1st play
Stuart A. Yeates wrote:
On 2/21/07, alain Baeckeroot [EMAIL PROTECTED] wrote:
Le mercredi 21 février 2007 02:10, Antonin Lucas a écrit:
No need for those difficulties, you can play along this board :
http://www.youdzone.com/go.html
I think this is not a torus, even if each vertice has 4
Jacques Basaldúa wrote:
Very good analysis and I would like to contribute a 4th reason:
As Luke Gustafson pointed out, we have to contemplate the simulation
as a _stochastic process_. We want to determine the conditional
probability of a win given a particular move is made. And that depends
on
Don Dailey wrote:
On Wed, 2007-02-07 at 11:34 +0100, Heikki Levanto wrote:
All this could be avoided by a simple rule: Instead of using +1 and -1
as the results, use +1000 and -1000, and add the final score to this.
Heikki,
I've tried ideas such as this in the past and it's quite
Magnus Persson wrote:
Quoting Matt Gokey [EMAIL PROTECTED]:
(snip)
Good point. This leads to another thought that I have been wondering
about. That is I question whether using more time to search more
simulations in the opening is the best approach. For the opening,
selecting
Eduardo Sabbatella wrote:
No please.
I use my email client, I sort them, I store them I'm
happy with it.
Personally, I will not be able to read the forum at
work. It will be the difference between reading and
not reading the list.
I want to choose which info will push me, and forget.
I
Upon continuing to learn about the general Monte Carlo field, I've found
it seems there is a general consensus in this community about a
distinction between Monte Carlo (MC) and what appears to be commonly
called Quasi Monte Carlo (QMC). MC is defined as using
random/pseudo-random distributions
ivan dubois wrote:
I dont understand how you can reduce the variance of monte-carlo sampling,
given a simulation can return either 0(loss) or 1(win).
Maybe it means trying to have mean values that are closer to 0 or 1 ?
Well strictly speaking I agree the standard models don't fit that well
-
It seems to me, the fundamental reason MC go (regardless of details)
works as it does is because it is the only search method (at least that
I am aware of) that has found a way to manage the evaluation problem.
Evaluation is not as problematic because MC goes to the bitter end
where the status is
David Doshay wrote:
I am a physics guy, and my thesis project was a large MC simulation.
The clusters that run SlugGo are usually busy doing MC simulations when
not playing Go.
In general MC needs to sample according to the proper distribution for
the problem. For some problems in quantum
[EMAIL PROTECTED] wrote:
-Original Message- From: [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] ...
The earliest MC engines were extremely simple and easily
described.
It seems inevitable that someone new to the field will seize on
this description, and then combine it with the success
Is MC Go a misnomer for programs in this genre not using simple random
playouts and combining with other techniques like pattern matching?
Technically, does the general Monte-Carlo method require random or
pseudo-random sampling?
If so, should we dub a new name for these non-random deep
Don Dailey wrote:
I was looking at many of the posts on the threads about how things
scale with humans and computers and I'm trying to reconcile many of
the various opinions and intuitions. I think there were many
legitimate points brought up that I appeared to be brushing off.
In computations
[EMAIL PROTECTED] wrote:
Yes. Don's scalability argument states that ELO gain is proportional
to time doubling.
For me scalable use of time implies that time translates into depth.
The extra depth is:
m - m0 = log(2)/log(b).
So if the ELO gain for time doubling in Chess equals 100 over a
Vlad Dumitrescu wrote:
Hi Matt,
On 1/25/07, Matt Gokey [EMAIL PROTECTED] wrote:
But just because a rule of thumb holds for Chess doesn't mean it does
for Go. Of course I could be wrong, but I was just trying to introduce
reasonable doubt, since Don always seems so sure of himself ;-)
If I
Ray Tayek wrote:
... I can say that I don't feel overwhelmed when playing chess. ...
Now with Go as a beginner still, on the other hand, I almost always
felt and still feel quite overwhelmed ...
yes, i usually feel this way in tournament games. and again more time
will help (for some
Been following this thread pretty closely and thought I would jump in
with a thought and try to find some common ground. I think there is
truth to be found in both sides of this argument.
Of course people improve with time and so do computers with certain
algorithms. The question is about
There may be some confusion about what the assumptions and goals are for
the CGOS pairing objectives. I am hearing conflicting statements. So I
for one am unsure ;-)
Don Daily wrote (from Re: [computer-go] A new pairing system idea for
CGOS, 10/8/2006):
Your basic idea is sound - but it's
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