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Compositionality in rational analysis: Grammar-based induction for concept learning

Author(s)
Goodman, Noah D.; Tenenbaum, Joshua B.; Griffiths, Thomas L.; Feldman, Jacob
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
This chapter provides a range of conceptual and technical insights into how this project can be attempted - and goes some way to suggesting that probabilistic methods need not be viewed as inevitably unable to capture the richness and complexity of world knowledge. It argues that structured representations, generated by a formal grammar, can be appropriate units over which probabilistic information can be represented and learned. This topic is likely to be one of the main challenges for probabilistic research in cognitive science and artificial intelligence over the coming decades. Keywords: probabilistic research; knowledge; grammar; concept learning; cognitive science; artificial intelligence
Date issued
2008
URI
https://hdl.handle.net/1721.1/124810
Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Journal
The Probabilistic Mind: Prospects for Bayesian Cognitive Science
Publisher
Oxford University Press
Citation
Goodman, Noah D. et al. "Compositionality in rational analysis: Grammar-based induction for concept learning." The Probabilistic Mind: Prospects for Bayesian Cognitive Science, edited by Nick Chater and Mike Oaksford, Oxford University Press, 2008. © 2008 Oxford University Press
Version: Author's final manuscript
ISBN
9780199216093

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