Integrating models of knowledge and machine learning

Abstract

We propose a framework allowing a real integration of Machine Learning and Knowledge acquisition. This paper shows how the input of a Machine Learning system can be mapped to the model of expertise as it is used in KADS methodology. The notion of learning bias will play a central role. We shall see that parts of it can be identified to what KADS's people call the inference and the task models. Doing this conceptual mapping, we give a semantics to most of the inputs of Machine Learning programs in terms of knowledge acquisition models. The ENIGME system which implements this work will be presented.