The error bars of all bots overlap. I'm not familiar enough with
BayesELO to compute p-values. I'd bet that only the 0.1 version has a
statistically significant strength difference.
Sent from my iPhone
On Oct 30, 2008, at 7:00 PM, Don Dailey <[EMAIL PROTECTED]> wrote:
The basic idea seems to be a modest improvement after 752 games. Note
that ALL versions with the incentive play stronger.
I'm going to try more aggressive values now - when I find a reasonable
value I'll try tanh() stuff.
Rank Name Elo + - games score oppo. draws
1 inc-0.1 2033 19 19 752 54% 2004 0%
2 inc-0.025 2008 19 19 750 49% 2012 0%
3 inc-0.01 2003 19 19 752 48% 2014 0%
4 mwNoDup-2000 2000 19 19 750 48% 2015 0%
On Thu, 2008-10-30 at 14:59 -0200, Mark Boon wrote:
Funny, I have been playing with something very similar. Although I
got side-tracked to something else for the moment. Intuitively I felt
tanh() was more appropriate than a linear function. Although you may
want to have the inverse of that, as I was trying to calculate the
territory certainty whereass you want the territory uncertainty.
Mark
On 30-okt-08, at 14:21, Don Dailey wrote:
Reference bot enhancement
=========================
Here is another possible enhancement to the reference bot which I am
currently testing. I do not yet have anything conclusive enough to
report, but it looks good so far with a small number of games.
But even if this idea doesn't pan out, it will produce a much more
natural playing style without weakening the bot.
Here is how it works. We will use 1000 playouts for our example:
1. Modify the bot to keep a "futures" table. At the end of each
playout, tally the wins for white and black for each point on the
board. (I tally -1 for a white win, 1 for a black win to get a
final score from -1000 to 1000 for each point.)
2. When the 1000 playouts are complete, compute an "uncertainty
value"
for each point, where 1.0 is completely uncertain, and 0.0 is
completely certain. A point is completely certain if at the end
of
each playout it was ALWAYS owned by one player or the other. It's
completely uncertain if it won 50% of the time for either side.
3. When determining which move to play, apply an uncertainty delta
to
the computed score of each move. This is simply some fraction of
the "uncertainty value" and the best value I've tested so far is
0.025. So you get a bonus that ranges from 0.0 to 0.025.
4. Choose the move with the best (sc + uncertainty_delta.)
5. The incentive must be small, large incentives will destroy the
playing strength. For instance 0.1 is too high and weakens it.
The value that is testing the best for me (of the ones I've tried
so far) is 0.025
6. This may test at some levels better than others. I'm testing
at 2000 playouts.
The idea is to gently encourage the bot to avoid playing to points
that are clearly a forgone conclusion (or conversely, encourage it
to
play where the "action" is.)
This should make the bot play much less artificially. Near the
end of
the game it will prefer moves to unresolved points. Earlier in the
game it will avoid moving to areas that are "probably" already won
or
lost.
My feeling is that these "incentives" should probably be
calculated in
a non-linear way, but what I described is a good starting point.
From
experiments in the past it seems more important to put the focus and
most of the weight on avoiding play to highly certain points. So I
will try some non-linear formula next.
- Don
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