Learning Search Control Rules for Planning: An Inductive Approach
Abstract
The computational complexity of planning has motivated significant efforts in machine learning. However, much of this work has concentrated on explanation-based learning techniques. An alternative approach is to use inductive learning. An inductive approach does not require a complete and tractable domain theory to be encoded and has the potential to create more effective rules by learning from more than one example at a time. In this paper, we describe an inductive system for learning search control rules and compare it with an existing explanation-based learning system.
Cite
Text
Leckie and Zukerman. "Learning Search Control Rules for Planning: An Inductive Approach." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50087-8Markdown
[Leckie and Zukerman. "Learning Search Control Rules for Planning: An Inductive Approach." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/leckie1991icml-learning/) doi:10.1016/B978-1-55860-200-7.50087-8BibTeX
@inproceedings{leckie1991icml-learning,
title = {{Learning Search Control Rules for Planning: An Inductive Approach}},
author = {Leckie, Christopher and Zukerman, Ingrid},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {422-426},
doi = {10.1016/B978-1-55860-200-7.50087-8},
url = {https://mlanthology.org/icml/1991/leckie1991icml-learning/}
}