Bibliographic Details
| Title: |
Closed-loop Teaching via Demonstrations to Improve Policy Transparency |
| Authors: |
Lee, Michael S., Simmons, Reid, Admoni, Henny |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computers and Society, Computer Science - Artificial Intelligence |
| More Details: |
Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, student learning may deviate from the preselected curriculum in situ. This paper thus explores augmenting a curriculum with a closed-loop teaching framework inspired by principles from the education literature, such as the zone of proximal development and the testing effect. We utilize tests accordingly to close to the loop and maintain a novel particle filter model of human beliefs throughout the learning process, allowing us to provide demonstrations that are targeted to the human's current understanding in real time. A user study finds that our proposed closed-loop teaching framework reduces the regret in human test responses by 43% over a baseline.
Comment: Supplementary material available at https://drive.google.com/file/d/1f_BDk3JpY6DvqlvgKtnQZ8zdfO3XAn3p/view?usp=drive_link |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2406.11850 |
| Accession Number: |
edsarx.2406.11850 |
| Database: |
arXiv |
