An Inductive Approach to Learning Search Control Rules for Planning
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1100-1104.
Abstract
One method for reducing the time required for plan generation is to learn search control rules from experience. Most of the recent work in learning search control knowledge has concentrated on explanationbased learning techniques. An alternative approach is to use inductive learning. An inductive approach does not require a complete and tractable domain theory and has the potential to create more effective rules by learning from more than one example at a time. In this paper, we describe Grasshopper, an inductive system that learns search control rules for a typical plan generation system. We also provide an empirical evaluation of Grasshopper by comparing it with an existing explanation-based learning system.
@inproceedings{ijcai1993p152,
title = {An Inductive Approach to Learning Search Control Rules for Planning},
author = {Leckie, Christopher and Zukerman, Ingrid},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1100--1104},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/152},
}
TY - CPAPER TI - An Inductive Approach to Learning Search Control Rules for Planning AU - Leckie, Christopher AU - Zukerman, Ingrid T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1100 EP - 1104 UR - https://ijcai.org/proceedings/1993/152 ER -Copied
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