QML-Morven : a framework for learning qualitative models
<p class="Abstract">The work proposed in this thesis continues the research into qualitative model learning (QML), a branch of qualitative reasoning. After the investigation of all existing qualitative model learning systems, especially the state-of-the-art system ILP-QSI,...
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ndltd-bl.uk-oai-ethos.bl.uk-4997012015-03-20T04:06:58ZQML-Morven : a framework for learning qualitative modelsPang, Wei2009<p class="Abstract">The work proposed in this thesis continues the research into qualitative model learning (QML), a branch of qualitative reasoning. After the investigation of all existing qualitative model learning systems, especially the state-of-the-art system ILP-QSI, a novel system named QML-Morven is presented. <p class="Abstract">QML-Morven inherits many essential features of the existing QML systems: it can learn models from positive only data, make use of the well-posed model constraints, process hidden variables, learn models from incomplete data, and perform systematic experiments to verify the hypotheses being made by researchers. <p class="Abstract">The development of QML-Morven allows us to further investigate some interesting yet unsolved questions in the QML research. As a result, four significant hypotheses are tested and validated by performing a series of systematic experiments with QML-Morven: 1. The information of state variables and the number of hidden variables are two important actors that can influence the learning, and the different combination of these two factors may give a different learning result in terms of the kernel subset (minimal data for a successful learning) and learning precision; 2. The scalability of QML may be improved by the use of an evolutionary algorithm; 3. For some models, the kernel subsets can be constructed by combining several sets of qualitative states, and the states in a kernel subset tend to scatter over the solution space; 4. The integration of domain-specific knowledge makes QML more applicable for learning the qualitative models of the real-world dynamic systems of high complexity. <p class="Abstract">The results and analysis of these experiments with respect to QML-Morven also raise many questions and indicates several new research directions. In the final part of this thesis, several possible future directions are explored.006.3Qualitative reasoning : Artificial intelligenceUniversity of Aberdeenhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499701http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=25499Electronic Thesis or Dissertation |
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006.3 Qualitative reasoning : Artificial intelligence |
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006.3 Qualitative reasoning : Artificial intelligence Pang, Wei QML-Morven : a framework for learning qualitative models |
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<p class="Abstract">The work proposed in this thesis continues the research into qualitative model learning (QML), a branch of qualitative reasoning. After the investigation of all existing qualitative model learning systems, especially the state-of-the-art system ILP-QSI, a novel system named QML-Morven is presented. <p class="Abstract">QML-Morven inherits many essential features of the existing QML systems: it can learn models from positive only data, make use of the well-posed model constraints, process hidden variables, learn models from incomplete data, and perform systematic experiments to verify the hypotheses being made by researchers. <p class="Abstract">The development of QML-Morven allows us to further investigate some interesting yet unsolved questions in the QML research. As a result, four significant hypotheses are tested and validated by performing a series of systematic experiments with QML-Morven: 1. The information of state variables and the number of hidden variables are two important actors that can influence the learning, and the different combination of these two factors may give a different learning result in terms of the kernel subset (minimal data for a successful learning) and learning precision; 2. The scalability of QML may be improved by the use of an evolutionary algorithm; 3. For some models, the kernel subsets can be constructed by combining several sets of qualitative states, and the states in a kernel subset tend to scatter over the solution space; 4. The integration of domain-specific knowledge makes QML more applicable for learning the qualitative models of the real-world dynamic systems of high complexity. <p class="Abstract">The results and analysis of these experiments with respect to QML-Morven also raise many questions and indicates several new research directions. In the final part of this thesis, several possible future directions are explored. |
author |
Pang, Wei |
author_facet |
Pang, Wei |
author_sort |
Pang, Wei |
title |
QML-Morven : a framework for learning qualitative models |
title_short |
QML-Morven : a framework for learning qualitative models |
title_full |
QML-Morven : a framework for learning qualitative models |
title_fullStr |
QML-Morven : a framework for learning qualitative models |
title_full_unstemmed |
QML-Morven : a framework for learning qualitative models |
title_sort |
qml-morven : a framework for learning qualitative models |
publisher |
University of Aberdeen |
publishDate |
2009 |
url |
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499701 |
work_keys_str_mv |
AT pangwei qmlmorvenaframeworkforlearningqualitativemodels |
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