Towards a General Framework for Composing Disjunctive and Iterative Macro-operators
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Machine Learning. Pages 596-601.
Abstract
Inducing disjunctive and iterative macro-operators from empirical problem-solving traces provides a more powerful knowledge compilation method than simple linear macro-operators. Whereas earlier work focused on w hen to create iterative macro-operators, this paper addresses how to form them, combining proven optimization methods such as extraction of loop invariants, with techniques for further optimizing RETEmatch efficiency. The disjunctive and iterative composition processes have been implemented in FERMI and its underlying production system language. Empirical results confirm substantial rule-match speedups and system performance improvements in different application domains.
@inproceedings{ijcai1989p95,
title = {Towards a General Framework for Composing Disjunctive and Iterative Macro-operators},
author = {Shell, Peter and Carbonell, Jaime G.},
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 = {596--601},
year = {1989},
month = {8},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1989/95},
}
TY - CPAPER TI - Towards a General Framework for Composing Disjunctive and Iterative Macro-operators AU - Shell, Peter AU - Carbonell, Jaime G. T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 596 EP - 601 UR - https://ijcai.org/proceedings/1989/95 ER -Copied
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