Eliciting good teaching from humans for machine learners

Cakmak, M., & Thomaz, A. L. (2014). Eliciting good teaching from humans for machine learners. Artificial Intelligence, 217, 198–215.


We propose using computational teaching algorithms to improve human teaching for machine learners. We investigate example sequences produced naturally by human teachers and find that humans often do not spontaneously generate optimal teaching sequences for arbitrary machine learners. To elicit better teaching, we propose giving humans teaching guidance, which are instructions on how to teach, derived from computational teaching algorithms or heuristics. We present experimental results demonstrating that teaching guidance substantially improves human teaching in three different problem domains. This provides promising evidence that human intelligence and flexibility can be leveraged to achieve better sample efficiency when input data to a learning system comes from a human teacher.

BibTeX Entry

  title = {Eliciting good teaching from humans for machine learners},
  author = {Cakmak, Maya and Thomaz, Andrea L.},
  journal = {Artificial Intelligence},
  volume = {217},
  pages = {198--215},
  year = {2014},
  publisher = {Elsevier}