Learning to tutor from expert demonstrators via apprenticeship scheduling
Name
Gombolay_AAAI_HMCL_2017.pdf
Description
Accepted version
Size
1.32 MB
Format
Adobe PDF
Checksum (MD5)
57ec4aaf337d3893a0835424295efe6e
Author(s)
Gombolay, Matthew
Jensen, Reed
Stigile, Jessica
Son, Sung-Hyun
Shah, Julie
Date Issued
March 2017
Journal
Workshops at the Thirty-First AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Gombolay, Matthew et al. "Learning to tutor from expert demonstrators via apprenticeship scheduling." Workshops at the Thirty-First AAAI Conference on Artificial Intelligence (March 2017): 664-669 © 2017 Association for the Advancement of Artificial Intelligence
Version
Author's final manuscript
Abstract
We have conducted a study investigating the use of automated tutors for educating players in the context of serious gaming (i.e., game designed as a professional training tool). Historically, researchers and practitioners have developed automated tutors through a process of manually codifying domain knowledge and translating that into a human-interpretable format. This process is laborious and leaves much to be desired. Instead, we seek to apply novel machine learning tech-niques to, first, leam a model from domain experts' demonstrations how to solve such problems, and, second, use this model to teach novices how to think like experts. In this work, we present a study comparing the performance of an automated and a traditional, manually-constructed tutor. To our knowledge, this is the first investigation using learning from demonstration techniques to learn from experts and use that knowledge to teach novices.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://www.aaai.org/ocs/index.php/WS/AAAIW17/paper/view/15098
