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Subject: Re: Opponent specific learning...

Author: Bruce Moreland

Date: 11:33:41 03/27/98

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On March 26, 1998 at 13:53:58, Jay Scott wrote:

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

The reason that "performance" programs don't do this is that they are
intended to be purchased/downloaded and used by one person.

If these programs were meant to play against communities of opponents,
they might have features of this sort.

The programs that play on the chess servers or the web, or commonly play
in tournaments with multiple opponents, may benefit from being set up to
do this.  Or perhaps not, since your opponents tend to learn from each
other's play against you.  In some cases you really are playing against
one entity -- the user community, which manifests itself in a variety of
forms, each with individual quirks, and each with a degree of ability to
learn from the other parts of the community.

But until now it seems that programmers assume they are playing the same
thing all the time, so they only need to direct their learning toward
that one thing.

It wouldn't be too challengint to allow learning against more than one
thing, simply by replicating the current learning data structures and
attaching a name field that can be filled in with the name of the

It might be annoying to do this, since the data structures might be
intermingled with larger data structures such as the opening book, but
it't not that big a deal to seperate this stuff out if you really want
to.  You just have to add another level of indirection.

Once you are able to replicate these data structures, and have a
convenient way of specifying who you are playing against, this problem
reduces again to the problem of learning against a single individual.


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