Reminder - it's tomorrow.
In message [EMAIL PROTECTED], Nick Wedd
[EMAIL PROTECTED] writes
The March 2007 KGS computer Go tournament will be next Sunday, April
8th, in the Asian evening, European morning and American night,
starting at 09:00 UCT and ending at about 13:00 UCT.
It will use
Jacques Basaldúa wrote:
Daniel Liu wrote:
An imperfect evaluation has errors. Is the exact value of the error
known? No.
I have an idea on that I will try to explain:
Given any finite combinatorial game where the ending nodes
have two possible values: win/loss, any node has a winning rate
On 4/7/07, Jacques Basaldúa [EMAIL PROTECTED] wrote:
Assuming two simplifying hypotheses:
1. The playouts are uniformly random.
2. Both players have the same number of legal moves (or any
unbalanced numbers compensate in the long term).
I did not understand your post either. Is #2 the same
Has anyone written anything reasonably accessible re
designing a neural net where the pulses themselves carry information (ie a
32-bit piece of bitmap, perhaps)
and/or
that information itself is used in determining whether a cell fires?
Forrest Curo
-
I have this idea that perhaps a good evaluation function could
replace the play-out portion of the UCT programs. The evaluation
function would return a value between 0 and 1 and would be an
estimate of the odds of winning.
I have tried this with an older and much weaker version of Suzie. It
To take a normal evaluation function and convert it to a
probability of winning function is probably difficult to
do well. You might have to map some sort of curve where
a few stones ahead represent a near win.
A simple approximation: - call the evaluation function - if
it is less than zero,
Personally, I could care less - I guess because I am a chess player and
I think it's weaker players who are impressed most by big wins. Very
strong chess players tend to take the long way around - taking the sure
win to the dramatic flashy quick but risky win. Weaker chess players
tend to
I don't understand your question. I don't claim non-determinism
helps with alpha beta and I'm not recommending a fuzzy evaluation
function, I'm just saying it still works. A deeper search will
produce better moves in general.
One has the randomness anyway. A heuristic evalution can be
There is a chapter in Ulf Lorenz Dissertation about this topic. Ulf mentions
this aspect also in the Hydra papers. E.g. the one for the XCell Journal.
Search on the net for Lorenz, Donninger, Hydra and format pdf. But in
this papers the concept is only mentioned without a detailed
At 06:17 AM 4/6/2007, you wrote:
On Fri, 2007-04-06 at 13:48 +0100, Jacques Basaldúa wrote:
Darren Cook wrote:
All except joseki-knowledge is board-size independent.
Maybe human player's adapt to different board sizes without
even noticing. But if you try to model strategy with
Up to my knowledge the first Lisp Versions had no number system. The number n
was represented as the list of numbers from 1 to n (which is also the
mathematical/axiomatic definition of the natural numbers).
But its not very practical. Can anyone provide me with a link how this was
done. I am
I don't have a reference, but it's probably a variant of Church
Numerals:
http://en.wikipedia.org/wiki/Church_numeral
On Apr 7, 2007, at 12:54 PM, Chrilly wrote:
Up to my knowledge the first Lisp Versions had no number system.
The number n was represented as the list of numbers from 1 to n
On Sat, 2007-04-07 at 21:54 +0200, Chrilly wrote:
Up to my knowledge the first Lisp Versions had no number system. The
number n was represented as the list of numbers from 1 to n (which is
also the mathematical/axiomatic definition of the natural numbers).
But its not very practical. Can
You are looking for a formalization of natural numbers.
The one you describe is probably a mangled description of the
construction from set theory.
AFAIK The natural Lisp construction is from the Peano axioms.
A shallow discourse:
mr. yang uses the ideas of short and long
extensions and high-low combinations in the
beginning. (a short extension being 1 or spaces
and a long being ideally 5 spaces). this tends to be eficient.
There is the classic Chinese rule of thumb on how far one can comfortably
extend along an edge:
I can turn the difficulty settings way down so that I have a chance to
actually win a game or two.
You can always decrease the time per move and at some limit, you'll reach
random play. Even if I can't win against MoGo with 300 playouts per move (I
am so bad :-( ), but can I beat a random
fyi:
http://scienceblogs.com/goodmath/2007/04/post_3.php
Today we're going to take our first baby-step into the land of surreal games.
A surreal number is a pair of sets {L|R} where
every value in L is less than every value in R.
If we follow the rules of surreal construction,
so that the
On Sat, 2007-04-07 at 14:36 -0400, Matt Kingston wrote:
What I want from a commercial go playing program is one that I can use
to learn to be a better go player. This brings up two important
deficiencies in the win by 0.5 strategy. If I'm always loosing by
half a point, It's difficult for me
A few weeks ago I announced that I was doing a long term
scalability study with computer go on 9x9 boards.
I have constructed a graph of the results so far:
http://greencheeks.homelinux.org:8015/~drd/public/study.jpg
Although I am still collecting data, I feel that I have
enough samples to
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