A Methodology for Evaluating Theory Revision Systems: Results with Audrey II
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 1128-1133.
Abstract
Theory revision systems are learning systems that have a goal of making small changes to an original theory to account for new data . A measure for the distance between two theories is proposed. This measure corresponds to the minimum number of edit operations at the literal level required to transform one theory into another. By computing the distance between an original theory and a revised theory, the claim that a theory revision system makes few revisions to a theory may be quantitatively evaluated. We present data using both accuracy and the distance metric on AUDREY II, a first-order theory revision system.
@inproceedings{ijcai1993p156,
title = {A Methodology for Evaluating Theory Revision Systems: Results with Audrey II},
author = {Wogulis, James and Pazzani, Michael J.},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {1128--1133},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/156},
}
TY - CPAPER TI - A Methodology for Evaluating Theory Revision Systems: Results with Audrey II AU - Wogulis, James AU - Pazzani, Michael J. T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 1128 EP - 1133 UR - https://ijcai.org/proceedings/1993/156 ER -Copied
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