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Published on Nov 11, 2013
Speaker: Charles Kemp Department of Psychology, Carnegie Mellon University
Abstract Bayes nets are not only useful for developing AI systems, but can also help to explain how humans reason under uncertainty. I will present a Bayes net framework for reasoning about multiple causal systems and will apply it to two aspects of human reasoning. The first application considers how people make inferences that depend on both causal relationships between features (e.g. animals with wings often fly) and similarity relationships between objects (e.g. eagles and hawks are rather similar). The second application explores how people make counterfactual inferences, or inferences about scenarios that differ from the real world in some respect.
Charles Kemp is an associate professor in CMU's psychology department. His research focuses on probabilistic models of human learning and inference.