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Learning Qualitative Differential Equation models: a survey of algorithms and applications

Published online by Cambridge University Press:  01 March 2010

Wei Pang  and
George M. Coghill
Wei Pang*
Affiliation:
Computational Intelligence Group, College of Computer Science and Technology, Jilin University, Changchun, P.R. China Department of Computing Science, School of Natural & Computing Sciences, University of Aberdeen, Aberdeen, UK
George M. Coghill*
Affiliation:
Department of Computing Science, School of Natural & Computing Sciences, University of Aberdeen, Aberdeen, UK
*
e-mail: pang.wei@abdn.ac.uk, pangwei@jlu.edu.cn, g.coghill@abdn.ac.uk
e-mail: pang.wei@abdn.ac.uk, pangwei@jlu.edu.cn, g.coghill@abdn.ac.uk
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Abstract

Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.

Type
Articles
Information
The Knowledge Engineering Review , Volume 25 , Issue 1 , March 2010 , pp. 69 - 107
Copyright
Copyright © Cambridge University Press 2010

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