[EMAIL PROTECTED]: <[EMAIL PROTECTED]>:
>> delta_komi = 10^(K * (number_of_empty_points / 400 - 1)),
>> where K is 1 if winnig and is 2 if loosing. Also, if expected
>> winning rate is between 45% and 65%, Komi is unmodified.
>
>There's one thing I don't like at all, there: you could get positive
>evaluation when losing, and hence play conservatively when you should
>not. That could especially be true near the end, when the situation
>drifted from bad to less bad. I think that if this happens you're
>doomed... The other way around would probably be painful too.
From my observaion, mc chooses good moves if and only if the winning
rate is near 50%. Once it gets loosing, it plays bad moves. Surely
it's an illusion but it helps to prevent them.
>After all, the aim of tinkering with komi is to avoid that the computer
>plays nonsensical moves, but it should know whether he must fight or
>calm down.
Agree. So, it's important _when_ adjust komi or apply my method. My
object is to keep winning rate around 50%, which yields good moves.
>Here is a suggestion: give a new komi at each move (not a drift, though
>it would probably be a good idea that you *upper bound the komi change*,
>maybe by ten times the delta_komi you use now).
>For choosing it, punish the fact of being away from true komi
>proportionnally to | komi_aya - true_komi |. Call lambda the
>proportionnality constant.
>Punish the winning rate through (winning_rate - .5)^2.
>Total loss function is then:
>loss = lambda * |komi_aya - true_komi| + (winning_rate - .5)^2
>
>Suppose the change in winning rate is proportional to change in komi,
>that is take as hypothesis:
>winning_rate(komi_aya) = winning_rate(normal_komi) + C (komi_aya -
>normal_komi)
>
>REMARK: C is negative !
>
>Take the change in komi that minimizes the punishment (a real), and
>truncate it to get an integer (truncation also means you have a tendancy
>to have delta_komi near 0).
>
>If you knew beforehand the winning rates for each komi, this would
>ensure you generally have a winning rate on the right side of .5, while
>it would not move komi if winning rate is somewhat near .5.
>
>Solving the degree two equation yields:
>change_in_winning_rate_by_point_komi_change =
>
>new_komi_aya =
> truncation[(.5 - old_winning_rate) +
> C * old_komi_aya - lambda / (2 * C)]^{+}
>
>if the estimated winning rate with true komi is more than .5, and
>
>new_komi_aya =
> truncation[(.5 - old_winning_rate) +
> C * old_komi_aya + lambda / (2 * C)]^{-}
> ^ ^
>if the estimated winning rate with true komi is less than .5.
>
>
>The estimated winning rate with true komi is given by
>winning_rate - C old_komi_aya.
>
>You could take
>lambda = K / (1 - number_of_empty_points / 400)
>if you want to be nearer true komi during yose. Less human-like, but
>less error-prone !
>Now that's probably not necessary since the constant C is probably much
>bigger when entering yose.
>
>
>The problem here in this formula is to find a good C, since this
>constant change during the game.
>Here is an idea from the data in the game: use the last time the komi
>was changed and take:
>C =
>(winning_rate_after_last_change - winning_rate_before_last_change) /
>(komi_aya_after_last_change - komi_aya_before_last_change)
>[* f(number_empty_points_last_change, number_empty_points_now) if you want to
>refine]
>
>Now this estimation might be unstable, especially since it cannot tell if
>that's the evolution of the situation that made the winning rate change,
>or the change in komi. I'm sure ou can find ways to stabilize. The
>easiest I can think of would be a formula like:
>new_C = .3 * C_formula_above + .7 old_C
>
>
>END OF THE DELIRIUM
:)
Thanks. I will try it later as I'm now rewriting my code radically
which will take so long.
# One question: where _aya_ comes from or stands for? If my guess is
correct, you are confusing Hiroshi, author of Aya, and I, Hideki,
author of GGMC :). I'm sorry if I'm wrong.
-Hideki
--
[EMAIL PROTECTED] (Kato)
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