>If the utility of any win is the same, it makes sense to
simply maximize the probability of winning. If we are not happy with
the program wasting points in a favorable endgame, it must be the case
that we are happier with a win by a large margin than with a win by a
small margin
I don't see a pro
>
> Why should this value be static? Shouldn't the behavior change when there
> is a certain win?
>
I think it should. This is what I do. When I have a win rate above 80% I
bias nodes by their average territory (counting prisoners). This might be
the same as adding a small bonus to each large win,
>
> We tried looking at local patterns and at board locations in 3x3 or
> large-knight's-move neighborhoods. Disappointingly, neither of these things
> helped.
>
I imagine that including patterns would have to use prior knowledge from
game records (or wherever). Maybe they should not look like inp
>
> I can't find the word "local" in the paper. Can you find the statement
> you're referring to?
>
My mistake. In 4.1 it says, "Moves were only considered if they were on the
3rd or 4th line or were within a large knightâs move of an existing stone."
I misread "existing" as "previous" somehow.
__
Sorry I should have proofread a bit better.
Or what about using this as a third term, like y[i] = w1[i]*m1 + w2[i]*m2 +
> w12[i]+m12 + b[i]?
>
I meant w12[i]*m12 for the third term.
One way to add patterns to the classifier might be to have input vectors for
> 3x3 patterns. Instead of a 1 at the
Thanks for the detailed explanation of the paper.
Would it make sense to vary the number of moves generated by the classifier
as you run more playouts? Have you tried this? It seems like the classifier
would return garbage initially and slowly give better moves deeper down the
sequence, analogous
Hi, long-time lurker and occasional poster here,
Thank you for the paper. I hope you don't mind me asking a few very basic
questions, since I am having trouble understanding exactly what you are
doing.
Let's say we are using a linear classifier. Then our output (the predicted
move) should look li
