This article presents a new research area called interactive task learning (ITL), in which an agent actively tries to learn not just how to perform a task better but the actual definition of a task through natural interaction with a human instructor while attempting to perform the task. The authors provide an analysis of desiderata for ITL systems, a review of related work, and a discussion of possible application areas for ITL systems.
@article{lairdetal17itl,
title={Interactive Task Learning},
author={John E. Laird and Kevin Gluck and John Anderson and Kenneth D. Forbus and Odest Chadwicke Jenkins and Christian Lebiere and Dario Salvucci and Matthias Scheutz and Andrea Thomaz and Greg Trafton and Robert E. Wray and Shiwali Mohan and James R. Kirk},
year={2017},
journal={IEEE Intelligent Systems},
issue={},
pages={6--21}
url={https://hrilab.tufts.edu/publications/lairdetal17itl.pdf}
doi={10.1109/MIS.2017.3121552}
}