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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 |
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| 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 > High-Performance Intelligent Computing School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > Pedagogical Research Group |
| Related URLs: | |
| Depositing User: | Sara Taylor |
| Date Deposited: | 29 Feb 2008 10:54 |
| Last Modified: | 27 Mar 2018 16:00 |
| URI: | http://eprints.hud.ac.uk/id/eprint/543 |
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