Abstract
Human beings are capable of learning to manually control complex nonlinear dynamical systems. It is well known, however, that humans have great difficulty in articulating the rules underlying their skilled behaviour. This paper focusses on the automatic machine induction of control rules from past records of skilled human behaviour. The aim of this endeavour is to install the induced rules as an automatic control program; it is anticipated that this will lead to more consistent and reliable control performance. The approach we study is based on the automatic induction of production rules from examples. The algorithms used are a product of the machine learning sub-field of artificial intelligence research. We present experimental results describing induction of executable models of skilled human control behaviour. Experiments were performed on physical laboratory apparatus.
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