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Computation of Context as a Cognitive Tool |
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Manon Sanscartier, Ph.D. Candidate Department of Computer Science University of Saskatchewan |
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In the field of cognitive science, as well as the area of Artificial Intelligence (AI), the role of context has been investigated in many forms, and for many purposes. It is clear in both areas that consideration of contextual information is important. However, the significance of context has not been emphasized in the Bayesian networks literature. We suggest that consideration of context is necessary for acquiring knowledge about a situation and for refining current representational models that are potentially erroneous due to hidden independencies in the data. We discuss how context-specific independencies in Bayesian networks and discovery algorithms, traditionally used for efficient probabilistic inference can contribute to the identification of contexts, and in turn can provide insight on otherwise puzzling situations. In the social sciences, the branch of attribution theory is context-sensitive. We suggest a method to distinguish between dispositional causes and situational factors by means of contextual models. Finally, we address the work of Cheng and Novick dealing with causal attribution by human adults. Their probabilistic contrast model makes use of contextual information, called focal sets, that must be determined by a human expert. We suggest a method for discovering complete focal sets from probabilistic distributions, without the human expert.
Manon Sanscartier is a Ph.D. candidate in Computer Science at the University of Saskatchewan, where she is supervised by Dr. Eric Neufeld. She holds a M.Sc. in Computer Science in Artificial Intelligence (AI) from the University of Regina, where she studied generalized forms of independencies in Bayesian networks, under the supervision of Dr. Cory Butz. Finally, she completed her undergraduate degree at the University of Ottawa, where she obtained a B.Com. with specialization in Management Information Systems (MIS). Her research interests include the semantics of contextual independencies discovered by means of tools in AI. The aim is to achieve a better understanding of the cognitive implications of independencies discovered in data.
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