Lars wrote:

> Anyone of you have similar or other experiences with the algorithm?

I use at runtime the same Bradley-Terry formulas Remí introduces
in his paper. That is a huge advance compared to "naif urgency"
scores because it gives a measure of how hard it was to "win"
for a move candidate. But I use a much simpler offline learning
algorithm: Compute the "naif" score just as standard:

 sc[0] = (times played)/(times played + times postponed)

Use this as an a priori value of the score. Then, iterating through
all the games many times, create a compensation weight CW
rewarding scores winning positions with high total of concurrent
scores and diminishing those won "too easily". Since the
efficiency of the offline part of the program is not an issue,
I make these in small steps iterating many times until they
do almost nothing. Each time, I compare observed versus
expected number of right guesses to see if it improves or not.

   sc[i] = CW*sc[i - 1]  // * is elem by elem mul of a vector

I guess it gets to more or less the same. Sure Remí's solution
is more efficient and elegant.


2 more issues I am concerned about patterns:
--------------------------------------------

MIAI URGENCY: When two (or maybe more) moves are
extremely urgent, but it is not important which of the two
because they are equivalent. In this case the high urgency
is masked by the fact that it is divided between two moves.


IMPLICITLY CHECKED URGENCY: When an urgent
pattern was already on the board and was not played its
score is overestimated. Imagine threatening a bamboo joint,
preserving the connection may be a huge point when it saves
a big group without eyespace. But it may also be worth nothing
when both groups are dead. When it first appears, there is
a high probability that it is big and, therefore, its urgency should
be high. But, if it wasn't played, then it is probably not big.
The next times it has to be considered much less urgent than
the first time.


Jacques.


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