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12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00)
The application of a machine learning tool to the validation of an air traffic control domain theory
Vancouver, British Columbia, Canada
November 13-November 15
ISBN: 0-7695-0909-6
ASCII Text
x 
 
M.M. West, T.L. McCluskey, "The application of a machine learning tool to the validation of an air traffic control domain theory," Tools with Artificial Intelligence, IEEE International Conference on, pp. 0414, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000.
 
 
BibTex
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@article{ 10.1109/TAI.2000.889902,
author = {M.M. West and T.L. McCluskey},
title = {The application of a machine learning tool to the validation of an air traffic control domain theory},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2000},
isbn = {0-7695-0909-6},
pages = {0414},
doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889902},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - The application of a machine learning tool to the validation of an air traffic control domain theory
SN - 0-7695-0909-6
SP
EP
A1 - M.M. West,
A1 - T.L. McCluskey,
PY - 2000
KW - learning (artificial intelligence); air traffic control; machine learning tool; air traffic control domain theory; IMPRESS; general clause form theories; ordinal sorts; geometrical revision operators
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
M.M. West, Sch. of Comput. & Math., Huddersfield Univ., UK
T.L. McCluskey, Sch. of Comput. & Math., Huddersfield Univ., UK
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.
Index Terms:
learning (artificial intelligence); air traffic control; machine learning tool; air traffic control domain theory; IMPRESS; general clause form theories; ordinal sorts; geometrical revision operators
Citation:
M.M. West, T.L. McCluskey, "The application of a machine learning tool to the validation of an air traffic control domain theory," ictai, pp.0414, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000
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