An Analytic Learning System for Specializing Heuristics
Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 922-928.
Abstract
This paper describes how meta-level theories are used for analytic learning in MuLTI-TAC. MULTI-TAC operationalizes generic heuristics for constraint-satisfaction problems, in order to create programs that are tailored to specific problems. For each of its generic heuristics, MULTI-TAC has a meta-theory specifically designed for operationalizing that heuristic. We present examples of the specialization process and discuss how the theories influence the tractability of the learning process. We also describe an empirical study showing that the specialized programs produced by MULTI-TAC compare favorably to hand-coded programs.
@inproceedings{ijcai1993p127,
title = {An Analytic Learning System for Specializing Heuristics},
author = {Minton, Steven},
booktitle = {Proceedings of the Thirteenth International Joint Conference on
Artificial Intelligence, {IJCAI-93}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ruzena Bajcsy},
pages = {922--928},
year = {1993},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1993/127},
}
TY - CPAPER TI - An Analytic Learning System for Specializing Heuristics AU - Minton, Steven T2 - Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, IJCAI-93 PB - International Joint Conferences on Artificial Intelligence PY - 1993 SP - 922 EP - 928 UR - https://ijcai.org/proceedings/1993/127 ER -Copied
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