Learning and using relational theories
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3332-learning-and-using-relational-theories.pdf
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255.74 KB
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Author(s)
Kemp, Charles
Goodman, Noah Daniel
Tenenbaum, Joshua B
Date Issued
December 2007
Journal
Advances in Neural Information Processing Systems (NIPS)
Publisher
Neural Information Processing Systems Foundation
Citation
Kemp, Charles et al. "Learning and using relational theories." Advances in Neural Information Processing Systems (NIPS), December 2007, Vancouver, B.C., Canada, Neural Information Processing Systems Foundation, December 2007 © 2007 Neural Information Processing Systems Foundation
Version
Final published version
Abstract
Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language. This complexity measure helps to explain how theories are learned from relational data, and how they support inductive inferences about unobserved relations. We describe two experiments that test our approach, and show that it provides a better account of human learning and reasoning than an approach developed by Goodman [1] .
Description
Kemp, Charles; Goodman, Noah D.; Tenenbaum, Joshua B.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/3332-learning-and-using-relational-theories
