A probabilistic model of theory formation.
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.
Kemp
Charles
C
Department of Psychology, Carnegie Mellon University, United States. ckemp@cmu.edu
Tenenbaum
Joshua B
JB
Niyogi
Sourabh
S
Griffiths
Thomas L
TL
D000465:Algorithms; D001499:Bayes Theorem; D003210:Concept Formation; D006801:Humans; D007858:Learning / Q000502:physiology; D008606:Mental Processes / Q000502:physiology*; D015233:Models, Statistical