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

West, Margaret M. and McCluskey, T.L. (2001) The application of machine learning tools to the validation of an air traffic control domain theory. International Journal on Artificial Intelligence Tools, 10 (4). pp. 613-637. ISSN 0218-2130

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Abstract

In this paper we describe a project (IMPRESS) in which machine learning (ML) tools were created and utilised for the validation of an Air Traffic Control domain theory written in first order logic. During the project, novel techniques were devised for the automated revision of general clause form theories using training examples. These techniques were combined in an algorithm which focused in on the parts of a theory which involve ordinal sorts, and applied geometrical revision operators to repair faulty component parts. While we illustrate the feasibility of applying ML to this area, we conclude that to be effective it must be focused to the application at hand, and used in mixed-initiative mode within a tools environment. The method is illustrated with experimental results obtained during the project.

Item Type: Article
Additional Information: UoA 23 (Computer Science and Informatics)
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
School of Computing and Engineering > Pedagogical Research Group
School of Computing and Engineering > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
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Depositing User: Sara Taylor
Date Deposited: 29 Feb 2008 10:54
Last Modified: 16 Dec 2010 13:28
URI: http://eprints.hud.ac.uk/id/eprint/543

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