Teaching Functions with Gaussian Process Regression

Humans are remarkably adaptive instructors who adjust advice based on their estimations about a learner’s prior knowledge and current goals. Many topics that people teach, like goal-directed behaviors, causal systems, categorization, and time-series patterns, have an underlying commonality: they map inputs to outputs through an unknown function. This project builds upon a Gaussian process (GP) regression model that describes learner behavior as they search the hypothesis space of possible underlying functions to find the one that best fits their current data. We extend this work by implementing a teacher model that reasons about a learner’s GP regression in order to provide specific information that will help them form an accurate estimation of the function.