Eliciting Knowledge Bases with Defeasible Reasoning: A Comparative Analysis with Machine Learning
作者： Peter Keogh
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摘要: Faculty Name DIT School of Computing MSc in (Advanced Software Development) Eliciting Knowledge Bases with Defeasible Reasoning: A Comparative Analysis Machine Learning by Peter KEOGH This thesis compares the ability an implementation Reasoning (via Argumentation Theory) to model a construct (mental workload) Learning. In order perform this comparison defeasible reasoning system was designed and implemented software. software used elicit knowledge base from expert experiment which then compared machine learning. The central findings were that based approach better at predicting objective performance measure, time, than However, learning equiped identify another object measure task membership. had high concurrent validity measures convergent existing mental workload.
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