Automated Generation of Connectionist Expert Systems for Problems Involving Noise and Redundancy
This work addresses the expensive and difficult task of building expert systems for problems with noise and redundancy, though it appears incremental as it modifies an existing process.
The paper tackled the challenge of constructing knowledge bases for expert systems in noisy and redundant data environments by modifying the MACIE process to dynamically generate training examples using deep and noise models, demonstrating its effectiveness with a small example that standard approaches struggle with.
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.