Le mercredi 22 novembre 2006 20:44, Rémi Coulom a écrit :
Hi,
I am in search of Go positions that are easy to understand for humans,
and difficult for computers.
Hi
Maybe too late ... Nethertheless, i remember a funny thing.
Some time ago i implemented opponent good move is good for me in
This is a good example of a reasonable heuristic leading to an
undesired solution after a correct evaluation. Most of the time
SlugGo did this it was correct that stopping the opponent's
monkey-jump was the biggest move on the board, so using
the heuristic was valuable. It just took things too
Le lundi 27 novembre 2006 16:36, Steven Clark a écrit :
Computer stupidity? How about how GNUGo has no problem invading under my 4,4
stone, but refuses to invade under my 5,5 stones? I assume this is because
there is a joseki entry for 4,4, but none for 5,5 openings. Attached is a
rather silly
alain Baeckeroot wrote:
Le mercredi 22 novembre 2006 20:44, Rémi Coulom a écrit :
Hi,
Hi Rémi
I am in search of Go positions that are easy to understand for humans,
and difficult for computers.
One incredibly simple example for human, where GNU Go horribly fails.
The only
Le samedi 25 novembre 2006 00:38, alain Baeckeroot a écrit :
Le mercredi 22 novembre 2006 20:44, Rémi Coulom a écrit :
Hi,
Hi Rémi
I am in search of Go positions that are easy to understand for humans,
and difficult for computers.
One incredibly simple example for human, where GNU
I can't help but feel we are missing something. With UCT we miss those
wonderful beta cutoff's that you get with straightforward alpha beta
pruning - but with alpha beta pruning you are still exploring a lot more
useless nodes.It seems the 2 are just not very compatible.
Currently UCT
Le mercredi 22 novembre 2006 20:44, Rémi Coulom a écrit :
Hi,
Hi Rémi
I am in search of Go positions that are easy to understand for humans,
and difficult for computers.
One incredibly simple example for human, where GNU Go horribly fails.
The only move is tengen (center of the board).
I
Le Jeudi 23 Novembre 2006 11:47, Chris Fant a écrit :
...
Its an interesting question if Monte-Carlo programms would also play
infinite strong.
I was under the impression that most MC implementations were
guaranteed to converge on the optimal move.
Yes, but it does not come from the MC
I think they will play very strong. Sofar all my tests indicates nice
scaling, but I admit I have not tried a proper experiment for a long time
since I do not have any extra hardware. Perhaps the Mogo team could do
something but the problem is that Mogo is so strong it would beat most
Sylvain,
The improvement over a given opponent should be measured by ELO points,
not win percentage unless you do the extra math. I cannot quite tell if
you were considering that or not - if so then ignore this. Going from
50% wins to 60% with is a modest improvement, but going from 80% to 90%
David Fotland wrote:
Many Faces plays L10, which looks like it also breaks both ladders.
-David
Thanks for testing.
What if Black replies with K9 ? It looks like K9 restores both ladders
(to my naive eye).
What about the first position I posted, where more tempting moves are
available
I think it's all in the presentation. Even if they are not even
beginners, with skill you can help them appreciate how some basic
concepts are difficult for a computer.
For instance, I think that you can teach the principle of 2 eyes with a
very simple example perhaps involving just 1 point
The attached position requires some basic understanding of Go. But its in my
experience a nasty problem. There is a simple rule for capture races. The
side with the higher number of liberties wins (if there are no eyes and more
=2 inside liberties involved). The side to move counts as a 1/2
Attached is another simple problem. A tactical-solver might work as this. It
checks all strings. If the number of liberties is below a thresold (e.g. 3)
it defines the string as a prey and tries to kill the string. Then the
solver takes the next prey, tries to kill it.
For this tactical solver
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