David Silver wrote:
I would be very interested to hear more about Dimwit's approach, and
also Remi's experiments with online learning in CrazyStone.
Hi,
My idea was very similar to what you describe. The program built a
collection of rules of the kind "if condition then move". Condition
could be anything from a "tree-search rule" of the kind "in this
particular position play x", or general rule such as "in atari,
extend".
It could be also anything in-between, such as a miai specific to the
current position. The strengths of moves were updated with an
incremental Elo-rating algorithm, from the outcomes of random
simulations.
I did not go very far in that direction, and my rule-based program is
still very weak. I found that I could bring very big improvements to
Crazy Stone with the techniques I described in my paper, so I
focused on
that. I will incorporate my patterns into the rule-based program in
the
future.
I found that my rule-based program scaled extremely well with larger
board sizes. What about yours ?
In theory I think it should scale well to larger boards. In 19x19 I
believe that full board positions become less important, and general
features of the board relatively more important.
But... in practice, I haven't got good results on larger boards. But
to be honest, I've focused much more on 9x9, so perhaps I've missed
some simple tricks.
-Dave
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