Showing versus Doing: Teaching by Demonstration
Mark K. Ho
Michael L. Littman
James MacGlashan
Fiery Cushman
Joseph L. Austerweil
Oct 2026
Artificial Intelligence
Machine Learning
Human-computer Interaction
Abstract
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration has been less well studied computationally. Here, we develop a Bayesian model for teaching by demonstration. Stark differences arise when demonstrators are intentionally teaching (i.e. showing) a task versus simply performing (i.e. doing) a task. In two experiments, we show that human participants modify their teaching behavior consistent with the predictions of our model. Further, we show that even standard IRL algorithms benefit when learning from showing versus doing.
