[net.ai] Generalization

DIETTERICH@SUMEX-AIM.ARPA (09/28/84)

From:  Tom Dietterich <DIETTERICH@SUMEX-AIM.ARPA>

Reply to Shebs' other flame:

"Induction...Not too hard really;"

Shebs comments are very naive.  Of course it isn't too hard to
construct a MECHANISM that sometimes performs inductive
generalizations properly.  However, every mechanism developed thus far
is very ad hoc.  They all rely on "having the right formalism".  In
other words, the programmer implicitly tells the program how to
generalize.  The programmer communicates a set of "biasses" or
preferences through the formalism.  Many of us working in inductive
learning suspect that general techniques will not be found until we
have a THEORY that justifies our generalization mechanisms.  The
justification of induction appears to be impossible.  Appeals to the
Principle of Insufficient Reason and Occam's Razor just restate the
problem without solving it.  In essence, the problem is: What is
rational plausible inference?  When you have no knowledge about which
hypothesis is more plausible, how do you decide that one hypothesis IS
more plausible?  A justification of inductive inference must rely on
making some metaphysical assertions about the nature of the world and
the nature of knowledge.  A justification for Occam's razor, for
example, must show why syntactic simplicity necessarily corresponds to
simplicity in the real world.  This can't be true for just any
syntactic representation!  For what representations is it true?

--Tom