Automated Generation of Connectionist Expert Systems for Problems
  Involving Noise and Redundancy
  When creating an expert system, the most difficult and expensive task is
constructing a knowledge base. This is particularly true if the problem
involves noisy data and redundant measurements. This paper shows how to modify
the MACIE process for generating connectionist expert systems from training
examples so that it can accommodate noisy and redundant data. The basic idea is
to dynamically generate appropriate training examples by constructing both a
'deep' model and a noise model for the underlying problem. The use of
winner-take-all groups of variables is also discussed. These techniques are
illustrated with a small example that would be very difficult for standard
expert system approaches.
