Learning and using relational theories
Author(s)Kemp, Charles; Goodman, Noah Daniel; Tenenbaum, Joshua B
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Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language. This complexity measure helps to explain how theories are learned from relational data, and how they support inductive inferences about unobserved relations. We describe two experiments that test our approach, and show that it provides a better account of human learning and reasoning than an approach developed by Goodman  .
Kemp, Charles; Goodman, Noah D.; Tenenbaum, Joshua B.
DepartmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Advances in Neural Information Processing Systems (NIPS)
Neural Information Processing Systems Foundation
Kemp, Charles et al. "Learning and using relational theories." Advances in Neural Information Processing Systems (NIPS), December 2007, Vancouver, B.C., Canada, Neural Information Processing Systems Foundation, December 2007 © 2007 Neural Information Processing Systems Foundation
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