Teaching Active Human Learners

Authors

  • Zizhe Wang Beihang University, China
  • Hailong Sun Beihang University, China

Keywords:

Learning of Cost, Reliability, and Skill of Label

Abstract

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.

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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. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16732

Issue

Section

AAAI Technical Track on Human-Computation and Crowd Sourcing