Abstract
In this report, a novel qualitative model learning (QML) framework named
QML-Morven is presented. QML-Morven is an extensible framework and currently includes three QML subsystems, which employ either symbolic or evolutionary approaches
as their learning strategies. QML-Morven uses the formalism of Morven, a fuzzy qualitative simulator, to represent and reason about qualitative models, and it also utilises
Morven to verify candidate models. Based on this framework, a series of experiments were
designed and carried out to: (1) verify the results obtained by the previous QML system
ILP-QSI; (2) investigate factors that in¿uence the learning precision and minimum data
requirement for successful learning; (3) address the scalability issue of QML systems.
QML-Morven is presented. QML-Morven is an extensible framework and currently includes three QML subsystems, which employ either symbolic or evolutionary approaches
as their learning strategies. QML-Morven uses the formalism of Morven, a fuzzy qualitative simulator, to represent and reason about qualitative models, and it also utilises
Morven to verify candidate models. Based on this framework, a series of experiments were
designed and carried out to: (1) verify the results obtained by the previous QML system
ILP-QSI; (2) investigate factors that in¿uence the learning precision and minimum data
requirement for successful learning; (3) address the scalability issue of QML systems.
| Original language | English |
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| Place of Publication | Aberdeen |
| Publisher | Department of Computing Science, University of Aberdeen |
| Number of pages | 37 |
| Publication status | Published - Jun 2012 |
Publication series
| Name | Technical Report ABDN–CS–12–03 |
|---|
Keywords
- qualitative reasoning
- qualitative model learning
- artificial immune systems
- backtracking with forward checking