Compositionality in rational analysis: Grammar-based induction for concept learning
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 represen...
Main Authors: | Goodman, Noah D., Tenenbaum, Joshua B., Griffiths, Thomas L., Feldman, Jacob |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
Format: | Article |
Language: | English |
Published: |
Oxford University Press
2020
|
Online Access: | https://hdl.handle.net/1721.1/124810 |
Similar Items
-
Learning Structured Generative Concepts
by: Stuhlmuller, Andreas, et al.
Published: (2017) -
Intuitive Theories as Grammars for Causal Inference
by: Tenenbaum, Joshua B., et al.
Published: (2021) -
Beyond Boolean logic: exploring representation languages for learning complex concepts
by: Piantadosi, Steven Thomas, et al.
Published: (2017) -
Fragment Grammars: Exploring Computation and Reuse in Language
by: O'Donnell, Timothy J., et al.
Published: (2009) -
Concepts in a Probabilistic Language of Thought
by: Goodman, Noah D., et al.
Published: (2015)