Acquiring Recursive Concepts with Explanation-Based Learning
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Machine Learning. Pages 688-692.
Abstract
Explanation-based generalization algorithms need to generalize the structure of their explanations. This is necessary in order to acquire concepts where a recursive or iterative process is implicitly represented in the explanation by a fixed number of applications. The fully-implemented BAGGER2 system generalizes explanation structures and produces recursive concepts when warranted. Otherwise the same result as standard explanation-based generalization algorithms is produced. BAGGER2's generalization algorithm is presented and empirical results that demonstrate the value of acquiring recursive concepts are reported. These experimental results indicate that generalizing explanation structures helps avoid the recently reported negative effects of learning. The advantages of the new approach over previous approaches that generalize explanation structures are described.
@inproceedings{ijcai1989p110,
title = {Acquiring Recursive Concepts with Explanation-Based Learning},
author = {Shavlik, Jude W.},
booktitle = {Proceedings of the Eleventh International Joint Conference on
Artificial Intelligence, {IJCAI-89}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {N. S. Sridharan},
pages = {688--692},
year = {1989},
month = {8},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1989/110},
}
TY - CPAPER TI - Acquiring Recursive Concepts with Explanation-Based Learning AU - Shavlik, Jude W. T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 688 EP - 692 UR - https://ijcai.org/proceedings/1989/110 ER -Copied
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