Learning causal schemata

Learning causal schemata (2007)

by Charles Kemp , Noah D. Goodman , Joshua B. Tenenbaum

Venue:	In Proceedings of the Twenty-ninth Annual Meeting of the Cognitive Science Society
Citations:	6 - 4 self

Datum	Value	Source
TITLE	Learning causal schemata	INFERENCE
AUTHOR NAME	Charles Kemp	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology	SVM HeaderParse 0.2
AUTHOR NAME	Noah D. Goodman	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology	SVM HeaderParse 0.2
AUTHOR NAME	Joshua B. Tenenbaum	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Brain and Cognitive Sciences; Massachusetts Institute of Technology	SVM HeaderParse 0.2
ABSTRACT	Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple causal schemata given only raw data as input. Given a set of objects and observations of causal events involving some of these objects, our framework simultaneously discovers the causal type of each object, the causal powers of these types, the characteristic features of these types, and the characteristic interactions between these types. Previous behavioral studies confirm that humans are able to discover causal schemata, and we show that our framework accounts for data collected by Lien and Cheng and Shanks and Darby.	SVM HeaderParse 0.2
YEAR	2007	INFERENCE
VENUE	In Proceedings of the Twenty-ninth Annual Meeting of the Cognitive Science Society	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	16 found	ParsCit 1.0