Combining Case-Based Reasoning, Explanation-Based Learning, and Learning Form Instruction
Abstract
Learning from instruction is a powerful technique for improving problem solving. An active student will predict the instructor's actions and then try to explain the differences from the predictions. We expand the concept of explanation beyond the provably correct explanations of Explanation-based learning to include other methods of explanation. The explanations can use deductions from causal domain knowledge, plausible inferences from the instructor's actions, previous cases of problem solving, and induction. The explanations result in improved diagnosis and improved future explanation. This combination of learning techniques leads to more opportunities to learn.
Cite
Text
Redmond. "Combining Case-Based Reasoning, Explanation-Based Learning, and Learning Form Instruction." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50010-2Markdown
[Redmond. "Combining Case-Based Reasoning, Explanation-Based Learning, and Learning Form Instruction." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/redmond1989icml-combining/) doi:10.1016/B978-1-55860-036-2.50010-2BibTeX
@inproceedings{redmond1989icml-combining,
title = {{Combining Case-Based Reasoning, Explanation-Based Learning, and Learning Form Instruction}},
author = {Redmond, Michael},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {20-22},
doi = {10.1016/B978-1-55860-036-2.50010-2},
url = {https://mlanthology.org/icml/1989/redmond1989icml-combining/}
}