Eliciting Knowledge Bases with Defeasible Reasoning: A Comparative Analysis with Machine Learning

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