Learning from Human Teachers: Issues and Challenges for ILP in Bootstrap Learning

Bootstrap Learning (BL) is a new machine learning paradigm that seeks to build an electronic student that can learn using natural instruction provided by a human teacher and by bootstrapping on previously learned concepts. In our setting, the teacher provides (very few) examples and some advice about the task at hand using a natural instruction interface. To address this task, we use our Inductive Logic Programming system called WILL to translate the natural instruction into first-order logic. We present approaches to the various challenges BL raises, namely automatic translation of domain knowledge and instruction into an ILP problem and the automation of ILP runs across different tasks and domains, which we address using a multi-layered approach. We demonstrate that our system is able to learn effectively in over fifty different lessons across three different domains without any humanperformed parameter tuning between tasks.