We present an algorithmic model for the development of children’s
intuitive theories within a hierarchical Bayesian framework,
where theories are described as sets of logical laws
generated by a probabilistic context-free grammar. Our algorithm
performs stochastic search at two levels of abstraction
– an outer loop in the space of theories, and an inner loop in
the space of explanations or models generated by each theory
given a particular dataset – in order to discover the theory
that best explains the observed data. We show that this model
is capable of learning correct theories in several everyday domains,
and discuss the dynamics of learning in the context of
children’s cognitive development.United States. Air Force Office of Scientific Research (AFOSR (FA9550-07-1-0075)United States. Office of Naval Research (ONR (N00014-09-0124)James S. McDonnell Foundation (Causal Learning Collaborative Initiative
