On 9, Feb 2007, at 4:40 AM, Sylvain Gelly wrote:
Alain's point, that knowledge can both help narrow the search to
good
moves and at the same time steer you away from the best move is
absolutely true in SlugGo's case.
I completely agree with that.
However can we agree that we want a better
Alain's point, that knowledge can both help narrow the search to good
moves and at the same time steer you away from the best move is
absolutely true in SlugGo's case.
I completely agree with that.
However can we agree that we want a better player in a whole, and not
only better in some
Le jeudi 8 février 2007 22:09, Sylvain Gelly a écrit :
It seems i was ambiguous: I was speaking of the simulation player too.
What i meant is a random simulation player is not biased, whereas a better
simulation player is biased by its knowledge, and thus can give wrong
evaluation of a
One simple explaination could be that a random player shamelessly tries all
moves (very bad ones but also very nice tesuji) whereas the stronger player
is restricted by its knowledge and will always miss some kind of moves.
Here we are not speeking about the pruning in the tree, but the
It seems i was ambiguous: I was speaking of the simulation player too.
What i meant is a random simulation player is not biased, whereas a better
simulation player is biased by its knowledge, and thus can give wrong
evaluation of a position.
I think we have to start defining what the bias. For
I think that the bias Alain meant is the choice of moves that control
the
branching factor. If I understand correctly, this can happen differently
in two places in MoGo: once in the branching below a node in the UCT
tree, and either the same or differently in the random playouts.
In some ways
