Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Machine Learning - special issue on inductive logic programming
Modelling of novices control skills with machine learning
UM '99 Proceedings of the seventh international conference on User modeling
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Understandable Learner Models for a Sensorimotor Control Task
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
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We present results of using inductive logic programming (ILP) to produce learner models by behavioural cloning. Models obtained using a program for supervised induction of production rules (ripper) are compared to models generated using a well-known program for ILP (foil). It is shown that the models produced by foil are either too specific or too general, depending on whether or not auxiliary relations are applied. Three possible explanations for these results are: (1) there is no way of specifying to foil the minimum number of cases each clause must cover; (2) foil requires that all auxiliary relations be defined extensionally; and (3) the application domain (control of a pole on a cart) has continuous attributes. In spite of foil's limitations, the models it produced using auxiliary relations meet one of the goals of our exploration: to obtain more structured learner models which are easier to comprehend.