title: A probabilistic model of theory formation.
abstract: Concept learning is challenging in part because the meanings of many concepts depend on their relationships to other concepts. Learning these concepts in isolation can be difficult, but we present a model that discovers entire systems of related concepts. These systems can be viewed as simple theories that specify the concepts that exist in a domain, and the laws or principles that relate these concepts. We apply our model to several real-world problems, including learning the structure of kinship systems and learning ontologies. We also compare its predictions to data collected in two behavioral experiments. Experiment 1 shows that our model helps to explain how simple theories are acquired and used for inductive inference. Experiment 2 suggests that our model provides a better account of theory discovery than a more traditional alternative that focuses on features rather than relations.
authors.affiliation: Department of Psychology, Carnegie Mellon University, United States. ckemp@cmu.edu
authors.forename: Charles
authors.initials: C
authors.lastname: Kemp
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
authors.forename: Sourabh
authors.initials: S
authors.lastname: Niyogi
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003210:Concept Formation; D006801:Humans; D007858:Learning / Q000502:physiology; D008606:Mental Processes / Q000502:physiology*; D015233:Models, Statistical