  When demonstrating a task, human tutors pedagogically modify their behavior
by either "showing" the task rather than just "doing" it (exaggerating on
relevant parts of the demonstration) or by giving demonstrations that best
disambiguate the communicated goal. Analogously, human learners pragmatically
infer the communicative intent of the tutor: they interpret what the tutor is
trying to teach them and deduce relevant information for learning. Without such
mechanisms, traditional Learning from Demonstration (LfD) algorithms will
consider such demonstrations as sub-optimal. In this paper, we investigate the
implementation of such mechanisms in a tutor-learner setup where both
participants are artificial agents in an environment with multiple goals. Using
pedagogy from the tutor and pragmatism from the learner, we show substantial
improvements over standard learning from demonstrations.
