Teaching Active Human Learners
DOI:
https://doi.org/10.1609/aaai.v35i7.16732Keywords:
Learning of Cost, Reliability, and Skill of LabelAbstract
Teaching humans is an important topic under the umbrella of machine teaching, and its core problem is to design an algorithm for selecting teaching examples. Existing work typically regards humans as passive learners, where an ordered set of teaching examples are generated and fed to learners sequentially. However, such a mechanism is inconsistent with the behavior of human learners in practice. A real human learner can actively choose whether to review a historical example or to receive a new example depending on the belief of her learning states. In this work, we propose a model of active learners and design an efficient teaching algorithm accordingly. Experimental results with both simulated learners and real crowdsourcing workers demonstrate that our teaching algorithm has better teaching performance compared to existing methods.Downloads
Published
2021-05-18
How to Cite
Wang, Z., & Sun, H. (2021). Teaching Active Human Learners. Proceedings of the AAAI Conference on Artificial Intelligence, 35(7), 5850-5857. https://doi.org/10.1609/aaai.v35i7.16732
Issue
Section
AAAI Technical Track on Human-Computation and Crowd Sourcing