Showing versus Doing: Teaching by Demonstration

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.

Published at NeurIPS 2016 (oral presentation). 12 pages, including supplementary materials

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)

arXiv:2610.14004 [cs.LG]

(or arXiv:2610.14004v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14004 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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