Combining Causal and Similarity-Based Reasoning
Abstract
Everyday inductive reasoning draws on many kinds of knowledge, including knowledge about relationships between properties and knowledge about relationships between objects. Previous accounts of inductive reasoning generally focus on just one kind of knowledge: models of causal reasoning often focus on relationships between properties, and models of similarity-based reasoning often focus on similarity relationships between objects. We present a Bayesian model of inductive reasoning that incorporates both kinds of knowledge, and show that it accounts well for human inferences about the properties of biological species.
Cite
Text
Kemp et al. "Combining Causal and Similarity-Based Reasoning." Neural Information Processing Systems, 2006.Markdown
[Kemp et al. "Combining Causal and Similarity-Based Reasoning." Neural Information Processing Systems, 2006.](https://mlanthology.org/neurips/2006/kemp2006neurips-combining/)BibTeX
@inproceedings{kemp2006neurips-combining,
title = {{Combining Causal and Similarity-Based Reasoning}},
author = {Kemp, Charles and Shafto, Patrick and Berke, Allison and Tenenbaum, Joshua B.},
booktitle = {Neural Information Processing Systems},
year = {2006},
pages = {681-688},
url = {https://mlanthology.org/neurips/2006/kemp2006neurips-combining/}
}