ML Anthology

Learning to Teach with a Reinforcement Learning Agent

Joseph Beck
AAAI 1998 pp. 1185
/aaai/1998/beck1998aaai-learning/

Abstract

Intelligent tutoring systems (ITS) use artificial intel-ligence techniques to customize their instruction to fit the needs of each student. To do this, the system must have knowledge of the student being taught (commonly called a student model) and a set of pedagogical rules that enable the system to follow good teaching prin-ciples. Teaching rules are commonly represented as a set of "if-then " production rules, where the "if " side is dependent on the student model, and the "then " side is a teaching action. For example, a rule may be of the form "IF (the student has never been introduced to the current topic) THEN (teach the topic to the student)." This approach is fairly straightforward from a knowl-edge engineering perspective, but has many drawbacks. First, there are many such rules, and it is very expensive

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Cite

Text

Beck. "Learning to Teach with a Reinforcement Learning Agent." AAAI Conference on Artificial Intelligence, 1998.

Markdown

[Beck. "Learning to Teach with a Reinforcement Learning Agent." AAAI Conference on Artificial Intelligence, 1998.](https://mlanthology.org/aaai/1998/beck1998aaai-learning/)

BibTeX

@inproceedings{beck1998aaai-learning,
  title     = {{Learning to Teach with a Reinforcement Learning Agent}},
  author    = {Beck, Joseph},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1998},
  pages     = {1185},
  url       = {https://mlanthology.org/aaai/1998/beck1998aaai-learning/}
}