The Automatic Training of Rule Bases that Use Numerical Uncertainty
  Representations
  The use of numerical uncertainty representations allows better modeling of
some aspects of human evidential reasoning. It also makes knowledge acquisition
and system development, test, and modification more difficult. We propose that
where possible, the assignment and/or refinement of rule weights should be
performed automatically. We present one approach to performing this training -
numerical optimization - and report on the results of some preliminary tests in
training rule bases. We also show that truth maintenance can be used to make
training more efficient and ask some epistemological questions raised by
training rule weights.
