ACQUIRING IMPLICIT KNOWLEDGE IN A COMPLEX-DOMAIN

This article describes GDCA-II, a machine learning-based system that acquires implicit knowledge through model abstraction in a flexible manufacturing system (FMS) domain. GDCA-II employs an integrated strategy involving conceptual clustering and case-based learning to acquire knowledge relevant for solving domain problems. Threshold values and bottleneck resource examples are used to demonstrate the necessity of acquiring implicit knowledge for improved decision making. Simulation results indicate that by acquiring implicit knowledge in problem solving considerable improvements in FMS performance can be achieved.