Teaching Active Human Learners


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


Learning of Cost, Reliability, and Skill of Label


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.




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



AAAI Technical Track on Human-Computation and Crowd Sourcing