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In S. Becker, S. Thrun, & K. Obermayer, eds. Advances in Neural Information Processing Systems 15. Cambridge, Mass.: The MIT Press. pp. 67-74.
 

Summary: In S. Becker, S. Thrun, & K. Obermayer, eds. Advances in Neural Information
Processing Systems 15. Cambridge, Mass.: The MIT Press. pp. 67-74.
Dynamical Causal Learning
David Danks Thomas L. Griffiths
Institute for Human & Machine Cognition Department of Psychology
University of West Florida Stanford University
Pensacola, FL 32501 Stanford, CA 94305-2130
ddanks@ai.uwf.edu gruffydd@psych.stanford.edu
Joshua B. Tenenbaum
Department of Brain & Cognitive Sciences
MIT
Cambridge, MA 02139
jbt@mit.edu
Abstract*
Current psychological theories of human causal learning and
judgment focus primarily on long-run predictions: two by
estimating parameters of a causal Bayes nets (though for different
parameterizations), and a third through structural learning. This
paper focuses on people's short-run behavior by examining
dynamical versions of these three theories, and comparing their

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics