Learning from instruction and experience: methods for incorporating procedural domain theories into knowledge-based neural networks

Abstract

This thesis defines and evaluates two systems that allow a teacher to provide instructions to a machine learner. My systems, FSkbann and ratle, expand the language that a teacher may use to provide advice to the learner. In particular, my techniques allow a teacher to give partially correct instructions about procedural tasks -- tasks that are solved as sequences of steps. FSkbann and ratle allow a computer to learn both from instruction and from experience. Experiments with these systems on several testbeds demonstrate that they produce learners that successfully use and refine the instructions they are given. In my initial approach, FSkbann, the teacher provides instructions as a set of propositional rules organized around one or more finite-state automata (FSAs). FSkbann maps the knowledge in the rules and FSAs into a recurrent neural network. I used FSkbann to refine the Chou-Fasman algorithm, a method for solving the secondary-structure prediction problem, a difficult task in mol...