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Subject: Re: Opponent specific learning...

Author: Jay Scott

Date: 10:53:58 03/26/98

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On March 26, 1998 at 13:04:34, Dan Homan wrote:

>It
>wouldn't be very hard to create a 'profile' for every player
>who plays my program and store information about the effectiveness
>of certain openings (and other things, as I think of them).

I don't know of any performance program that does this. The AI
programs that do opponent modeling have been pretty clumsy about it
so far, in my view.

I think the interesting question is, what information will it do
your program the most good to learn? It's easy to learn what
openings the opponent plays badly, and aim for them, but that
info is very specific. It would be more interesting to learn,
for example, that player X folds up under violent attack and
player Y can't cope with rook endings. But also harder, of course.

My idea is that the more general the information that you learn,
the more often you can use it, so the more useful it is. One
possibility is to try to categorize your opponents and learn
about the categories. The first category split could be
human/computer opponent. Another split could be relative tendency
to make mistakes, or uncover your program's mistakes, in the opening,
middle game, or ending: tabulate the material on the board when there
are big score shifts, and use the info in deciding whether to
trade down. Another could be playing strength relative to your
program; maybe stronger opponents should be treated differently
than weaker ones. I'm sure you can think of more. The advantage
of the category approach is that as soon as you can assign an
opponent to a category, you know that much more about them--and
you may even be able to do it by looking at their past games,
before meeting the opponent at all. Plus, nothing in the category
approach stops you from doing individual opponent learning too.

  Jay



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