On Tue, 2008-04-01 at 15:18 -0400, Joshua Shriver wrote:
Do you have a link to those papers?
There is still one listed on the computer go bibliograpy:
http://www.cs.ualberta.ca/~games/go/compgo_biblio/
The links don't seem to work, so I set up a copy here:
Jonas Kahn wrote:
I guess you have checked that with your rules for getting probability
distributions out of gammas, the mean of the probability of your move 1
was that that you observed (about 40 %) ?
If I understand your post, there may be a misunderstanding by my fault.
Here gamma is not
On 1-apr-08, at 17:37, Don Dailey wrote:
That's partly why I'm interested in exploring on the fly leaning.
Learning outside the context of the position being played may not have
much relevance.
That would be most interesting indeed. I'd like to try but keep
running into obstacles.
For
On Wed, Apr 02, 2008 at 02:13:45PM +0100, Jacques Basaldúa wrote:
Jonas Kahn wrote:
I guess you have checked that with your rules for getting probability
distributions out of gammas, the mean of the probability of your move 1
was that that you observed (about 40 %) ?
If I understand your
By contrast, you
should test (in the tree) a kind of move that is either good or average,
but not either average or bad, even if it's the same amount of
information. In the tree, you look for the best move. Near the root at
least; when going deeper and the evaluation being less precise,
Mark Boon wrote:
On 1-apr-08, at 17:37, Don Dailey wrote:
That's partly why I'm interested in exploring on the fly leaning.
Learning outside the context of the position being played may not have
much relevance.
That would be most interesting indeed. I'd like to try but keep
running into
So I believe a better approach is a heavy playout approach with NO
tree. Instead, rules would evolve based on knowledge learned from each
playout - rules that would eventually move uniformly random moves into
highly directed ones. All-moves-as-first teaches us that in the
general case
