(Georgia Institute of Technology, 2010)
Zang, Peng; Tian, Runhe; Thomaz, Andrea L.; Isbell, Charles L.
Agents that operate in human environments will
need to be able to learn new skills from everyday people.
Learning from demonstration (LfD) is a popular paradigm for
this. Drawing from our interest in Socially Guided Machine
Learning, we explore the impact of interactivity on learning
from demonstration. We present findings from a study with
human subjects showing people who are able to interact with
the learning agent provide better demonstrations (in part) by
adapting based on learner performance which results in improved
learning performance. We also find that interactivity increases a
sense of engagement and may encourage players to participate
longer. Our exploration of interactivity sheds light on how best
to obtain demonstrations for LfD applications.