Invented Predicates to Reduce Knowledge Acquisition

The aim of this study was to develop machine-learning techniques that would speed up knowledge acquisition from an expert. As the expert provided knowledge the system would generalize from this knowledge in order to reduce the need for later knowledge acquisition. This generalization should be completely hidden from the expert. We have developed such a learning technique based on Duce’s intra-construction and absorption operators [1] and applied to Ripple-Down Rule (RDR) incremental knowledge acquisition [2]. Preliminary evaluation shows that knowledge acquisition can be reduced by up to 50%.