John Tromp wrote:
On 5/18/07, Rémi Coulom <[EMAIL PROTECTED]> wrote:
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.
The obvious way to update weights is to reward all the
rules that fired for the winning side, and penalize all rules that
fired for
the losing side, with rewards and penalties decaying toward the end
of the playout. But this is not quite Elo like, since it doesn't
consider rules
to beat each other. So one could make the reward dependent on the
relative
weight of the chosen rule versus all alternatives. increasing the
reward if the
alternatives carried a lot of weight.
Is that how your ratings worked?
It is Elo-like in the generalized Bradley-Terry sense I describe in my
paper: you have one team of one color beating one team of the other
color. What I do exactly is compute the total Elo rating of black moves
(with a decay so that clean-up moves don't count, and moves close to the
root count more), and the total Elo rating of white moves. Then I
compute the difference between the real outcome and the expected outcome
according to Elo ratings, and correct Elo ratings proportionally to that
difference.
Rémi
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