Uncertainty in Artificial Intelligence
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The Cognitive Processing of Causal Knowledge
Scott Morris, Doug Cork, Richard Neapolitan
Abstract:
There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learning causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concerning how the presence of one cause of an effect makes another cause less probable) support the hypothesis that humans reach the same judgments as algorithms for doing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and dependencies as those used by certain algorithms for learning the structure of a Bayesian network. Based on this indication, a subjective definition of causality is forwarded. Finally, methods for further testing the accuracy of these claims are discussed.
Keywords: Bayesian network, causes, cognition, Piaget.
Pages: 384-391
PS Link:
PDF Link: /papers/97/p384-morris.pdf
BibTex:
@INPROCEEDINGS{Morris97,
AUTHOR = "Scott Morris and Doug Cork and Richard Neapolitan",
TITLE = "The Cognitive Processing of Causal Knowledge",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "384--391"
}


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