The Statistical Learning of Accurate Heuristics
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1079-1086.
Abstract
Heuristics used by search algorithms are usually composed of more primitive functions which we call "features". A method for combining features is presented which is based on linear regression with true distance to goal as the dependent variable. Our method also provides a probabilistic estimate of the solution error produced when the combination of features is used as a heuristic by A •. The accuracy of the heuristics learned is demonstrated in the TSP and sliding tile domains. The high quality solutions returned and the considerable savings in nodes expanded in both domains show effectiveness as well as generality of the method.
@inproceedings{ijcai1993p149,
title = {The Statistical Learning of Accurate Heuristics},
author = {Bramanti-Gregor, Anna and Davis, Henry W.},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1079--1086},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/149},
}
TY - CPAPER TI - The Statistical Learning of Accurate Heuristics AU - Bramanti-Gregor, Anna AU - Davis, Henry W. T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1079 EP - 1086 UR - https://ijcai.org/proceedings/1993/149 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
