Selectively Generalizing Plans for Problem-Solving
Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Learning and Acquisition. Pages 596-598.
Abstract
Problem solving programs that generalize and save plans in order to improve their subsequent performance inevitably face the danger of being overwhelmed by an ever-increasing number of stored plans. To cope with this problem, methods must be developed for selectively learning only the most valuable aspects of a new plan. This paper describes MORRIS, a heuristic problem solver that measures the util.ity of plan fragments to determine whether they are worth learning. MORRIS generalizes and saves plan fragments if they are frequently used, or if they are helpful in solving difficult subproblems. Experiments are described comparing the performance of MORRIS to a less selective learning system.
@inproceedings{ijcai1985p114,
title = {Selectively Generalizing Plans for Problem-Solving},
author = {Minton, Steven},
booktitle = {Proceedings of the Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-85}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Aravind Joshi},
pages = {596--598},
year = {1985},
note = {Learning and Acquisition},
url = {https://ijcai.org/proceedings/1985/114},
}
TY - CPAPER TI - Selectively Generalizing Plans for Problem-Solving AU - Minton, Steven T2 - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85 PB - International Joint Conferences on Artificial Intelligence PY - 1985 SP - 596 EP - 598 UR - https://ijcai.org/proceedings/1985/114 ER -Copied
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