Combining Weak Learning Heuristics in General Problem Solvers
Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Knowledge Acquisition. Pages 331-332.
Abstract
This paper is concerned with state space problem solvers that achieve generality by learning strong heuristics through experience in a particular domain. We specifically consider two ways of learning by analysing past solutions that can improve future problem solving: creating macros and the chunks. A method of learning search heuristics is specified which is related to 'chunking' but which complements the use of macros within a goal directed system. An example of the creation and combined use of macros and chunks, taken from an implemented system, is described.
@inproceedings{ijcai1987p68,
title = {Combining Weak Learning Heuristics in General Problem Solvers},
author = {McCluskey, Thomas Leo},
booktitle = {Proceedings of the Tenth International Joint Conference on
Artificial Intelligence, {IJCAI-87}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John McDermott},
pages = {331--332},
year = {1987},
note = {Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1987/68},
}
TY - CPAPER TI - Combining Weak Learning Heuristics in General Problem Solvers AU - McCluskey, Thomas Leo T2 - Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 PB - International Joint Conferences on Artificial Intelligence PY - 1987 SP - 331 EP - 332 UR - https://ijcai.org/proceedings/1987/68 ER -Copied
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