The aim of this study was to develop machine
learning techniques that would facilitate knowledge
acquisition from an expert by taking over
the knowledge engineering task of identifying
intermediate abstractions. As the expert provided
knowledge the system would generalize
from this knowledge and use the abstractions it
learned in order to reduce the need for later
knowledge acquisition. This generalization
should be invoked automatically and be completely
hidden from the expert manually adding
knowledge. We have developed such a learning
technique based on Duce’s intra construction and
absorption operators [Muggleton, 1990] and applied
to Ripple Down Rules (RDR) incremental
knowledge acquisition [Compton & Jansen,
1990]. Preliminary evaluation shows that this
mixed initiative approach reduces knowledge
acquisition effort by up to 50%.