This is happening everyday for me. My IP is not changing. I don't
think it's a lag issue. But I could be wrong. Is it possible that
there is a bug in the Windows TCL interpreter? How many other people
out there are running TCL on Windows for cgos?
On 2/6/07, Magnus Persson [EMAIL
To who it may concern:
ggexp appears to be losing all of it's games on time.
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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
It seems that there are at least three cases:
1: Choosing a random move from a uniform distribution
2: Choosing a random move from a nonuniform distribution (patterns etc.)
3: Choosing a move taking into account what has been chosen before
The concensus seems to be that numbers 1 and 2 are MC and
I just checked this for January and here are the statics:
When playing white ggexp played:
1087 games
295 losses
8 of these were time losses.
When playing black ggexp played
1036 games
341 losses
17 losses
So I don't see that it's losing all
It lost several games in a row on time at the time that I sent that
message. Obviously, it can't have lost ALL of it's games and still
attained an 1800 rating.
On 2/6/07, Don Dailey [EMAIL PROTECTED] wrote:
I just checked this for January and here are the statics:
When playing white ggexp
On Tue, 2007-02-06 at 14:16 -0500, Chris Fant wrote:
It lost several games in a row on time at the time that I sent that
message. Obviously, it can't have lost ALL of it's games and still
attained an 1800 rating.
I assumed that you meant that of all the games it lost, they were
mostly due to
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 that there are at least three cases:
1: Choosing a random move from a uniform distribution
2: Choosing a random move from a nonuniform distribution (patterns etc.)
3: Choosing a move taking into account what has been chosen before
The concensus seems to be that numbers 1 and 2 are MC
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
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