The application of a machine learning tool to the validation of an air traffic control domain theory

Abstract

In this paper we describe a project (IMPRESS) which utilised a machine learning tool for the validation of an air traffic control domain theory. During the project, novel techniques were devised for the automated revision of general clause form theories using training examples. This technique involves focusing in on the parts of a theory which involve ordinal sorts, and applying geometrical revision operators to repair faulty component parts. The method is illustrated with experimental results obtained during the project.