Thanks everyone for the help thus far. I have been looking at the GTP protocol
page and I am curious which version of the protocol I should try to implement
if I want to communicate with the servers. Should I be looking at the GTP 2.0
draft version?
Thanks in advance,
Carter.
Carter Cheng wrote:
Thanks everyone for the help thus far. I have been looking at the GTP protocol
page and I am curious which version of the protocol I should try to implement
if I want to communicate with the servers. Should I be looking at the GTP 2.0
draft version?
You should implement
On Wed, 2009-07-15 at 11:24 +0200, Urban Hafner wrote:
Carter Cheng wrote:
Thanks everyone for the help thus far. I have been looking at the GTP
protocol page and I am curious which version of the protocol I should
try to implement if I want to communicate with the servers. Should I be
Where can I find information on these bridging protocols or are libraries
provided for this (to the 9x9 19x19 servers)?
--- On Wed, 7/15/09, Hellwig Geisse hellwig.gei...@mni.fh-giessen.de wrote:
From: Hellwig Geisse hellwig.gei...@mni.fh-giessen.de
Subject: Re: [computer-go] gtp which
For KGS, there is kgsgtp.jar, CGOS provides scripts that connect your
engine to the server, too.
Am 15.07.2009 um 15:41 schrieb Carter Cheng:
Where can I find information on these bridging protocols or are
libraries provided for this (to the 9x9 19x19 servers)?
--- On Wed, 7/15/09,
On Wed, Jul 15, 2009 at 9:41 AM, Carter Cheng carter_ch...@yahoo.comwrote:
Where can I find information on these bridging protocols or are libraries
provided for this (to the 9x9 19x19 servers)?
The CGOS protocol is pretty easy to decode from the cgos client script which
is written in TCL.
thaoeuns at gmail.com wrote:
So changing the komi doesn't actually improve your confidence
interval. If (as Darren said) the win percentage is a crude
estimate of the final score, then changing komi would do nothing
to change the results one got (and at extremes biases it badly).
Moving
When using patterns during the playout I had improvised some code to
select patterns randomly, but favour those with higher weights more or
less proportionally to the weight..
How many patterns, and are the weights constant for the whole game?
If relatively few, and constant, you can make a
You might look in the genetic algorithm literature, where they have to
do this for fitness-proportional reproduction. A useful buzzword is
roulette wheel.
Peter Drake
http://www.lclark.edu/~drake/
On Jul 15, 2009, at 4:06 PM, Mark Boon wrote:
When using patterns during the playout I had
If your weights are all between 0 and 1:
do
double r = rand(0 to 1)
int i = rand(0 to weightCount - 1)
until weight[i] r
I think that's right.
Mark Boon wrote:
When using patterns during the playout I had improvised some code to
select patterns randomly, but favour those with higher
I think you could do this with a binary tree - at each node keep a total of
the weight values of the subtree below the node.
If the pattern was hashed, then each bit could define a branch of the tree,
0 = left branch 1 = right branch.
Then you have a very simple divide and conquer algorithm.
So many complex ideas :) Why not just multiply the weight of each pattern
by a random number and pick the biggest result?
David
-Original Message-
From: computer-go-boun...@computer-go.org [mailto:computer-go-
boun...@computer-go.org] On Behalf Of Mark Boon
Sent: Wednesday, July 15,
So many complex ideas :) Why not just multiply the weight of each pattern
by a random number and pick the biggest result?
Good for 5 patterns, not so good for 5000 patterns.
Darren
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You would only do this for patterns that match in a position. For Many
Faces that is typically a few dozen. Many Faces only has about 1800 total
patterns in its go knowledge base. Playouts use Mogo patterns, about a
dozen total.
David
-Original Message-
From:
On Wed, Jul 15, 2009 at 10:37 PM, David Fotlandfotl...@smart-games.com wrote:
So many complex ideas :) Why not just multiply the weight of each pattern
by a random number and pick the biggest result?
David
That involves generating N random numbers and then doing N-1
comparisons. The n-ary
I must be missing something. Isn't the obvious trick:
int r = random(sum of weights);
int i = 0;
while (r weights[i]) {
r -= weights[i];
}
return i;
This way, you only have to generate one random number.
Peter Drake
http://www.lclark.edu/~drake/
On Jul 15, 2009, at 8:55 PM, Zach
In the loop i is always zero. I think your code is wrong.
You probably meant to loop over all the weights (or I should say on average
half the weights), and this code is slow if there are a lot of weights.
2009/7/16 Peter Drake dr...@lclark.edu
I must be missing something. Isn't the obvious
On Wed, Jul 15, 2009 at 11:37 PM, David Fotland fotl...@smart-games.comwrote:
So many complex ideas :) Why not just multiply the weight of each pattern
by a random number and pick the biggest result?
This is fine if you are looking for the slowest algorithm you can find.
But it does have the
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