Pedagogical Demonstrations and Pragmatic Learning in Artificial Tutor-Learner Interactions
This work addresses the challenge of efficient learning from demonstrations for artificial agents, though it appears incremental as it builds on existing LfD frameworks.
The paper tackles the problem of sub-optimal demonstrations in Learning from Demonstration (LfD) by implementing pedagogical and pragmatic mechanisms in artificial tutor-learner interactions, resulting in substantial improvements over standard methods.
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.