The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Case-based Reasoning. Pages 232-236.
Abstract
Structure-mapping is a provably NP-Hard problem which is argued to lie at the core of the human metaphoric and analogical reasoning faculties. This NP-Hardness has meant that early attempts at optimal solutions to the problem have had to be augmented with sub-optimal heuristics to ensure tractable performance. This paper considers various grounds for qualifying the competence of such heuristic approaches, and offers an evaluation of the sub-optimal performance of three different models of analogy, SME, ACME and Sapper.
@inproceedings{ijcai1997p35,
title = {The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies},
author = {Veale, Tony and Keane, Mark T.},
booktitle = {Proceedings of the Fifteenth International Joint Conference on
Artificial Intelligence, {IJCAI-97}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Martha E. Pollack},
pages = {232--236},
year = {1997},
note = {Case-based Reasoning},
url = {https://ijcai.org/proceedings/1997/35},
}
TY - CPAPER TI - The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies AU - Veale, Tony AU - Keane, Mark T. T2 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97 PB - International Joint Conferences on Artificial Intelligence PY - 1997 SP - 232 EP - 236 UR - https://ijcai.org/proceedings/1997/35 ER -Copied
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