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From: jones@amarna.gsfc.nasa.gov (JONES, THOMAS)
Newsgroups: comp.ai.philosophy
Subject: Machine learning
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Date: 11 Dec 90 02:42:02 GMT
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Dear comp.ai.philosophy,

The question has been raised as to whether or not we could put a learning or
"reinforcement" algorithm, perhaps along the lines of Skinner's concepts, and
make the machine *learn* all sorts of neat things without their having to
be programmed in by humans.  This is the oldest, worst idea in AI.  Dozens
of attempts have been made to carry this effort through (I myself have made
a dozen or so.), essentially without success.  The problem is that *all*
theories of learning in psychology are *unsound.*  For example, Skinner
would have us believe that, if an organism is rewarded for doing a certain
action in a certain situation, then he/she/it will become more likely to perform
the action in question in "similar" situations.  Horsefeathers!  What is a
similar action?  What is a similar situation?  All sorts of heavy machinery
have been swept under the rug and labeled "similarity."

    From the above it might be concluded that I am opposed to machine learning
in general.  On the contrary, I consider it one of the most important areas
of AI.  One bad habit which afflicts learning research is the failure to
distinguish between that which the machine can legitimately learn for itself,
and that which the human programmers jolly well better program in by hand.
For example, I doubt very much if a machine could do more than a few 
rudimentary things without the concept of a *subroutine hierarchy* (or the
related GPS goal tree).  Hence I believe that the machine should have
software for building up, testing, and using subroutine hierarchies on
its own.  But can the machine *invent* subroutine hierarchies?  Doubt.
Much of the *nerve net* research is marred by lack of making this distinction.
My experience with learning codes is that you start by working out just how
the performance program is to look and to operate.

References:

Jones, Thomas L., "A Computer Model of Simple Forms of Learning," MIT Ph.D.
thesis, September, 1970.

Jones, Thomas L., "A Computer Model of Simple Forms of Learning in Infants,"
Proc. AFIPS 1972 Spring Joint Computer Conference.

Tom Jones

All opinions are my own.