The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies
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 earlier nave attempts at optimal solutions to the problem have had to be augmented with sub-optimal heuristics to ensure tractable performance. This paper considers various epistemological grounds for qualifying the competence of such heuristic approaches, and offers a quantitative evaluation of the suboptimal performance of three different models of analogy/metaphor, SME, ACME and Sapper.
Cite
Text
Veale and Keane. "The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies." International Joint Conference on Artificial Intelligence, 1997.Markdown
[Veale and Keane. "The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies." International Joint Conference on Artificial Intelligence, 1997.](https://mlanthology.org/ijcai/1997/veale1997ijcai-competence/)BibTeX
@inproceedings{veale1997ijcai-competence,
title = {{The Competence of Sub-Optimal Theories of STructure Mapping on Hard Analogies}},
author = {Veale, Tony and Keane, Mark T.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1997},
pages = {232-237},
url = {https://mlanthology.org/ijcai/1997/veale1997ijcai-competence/}
}