  Much of human learning and inference can be framed within the computational
problem of relational generalization. In this project, we propose a Bayesian
model that generalizes relational knowledge to novel environments by
analogically weighting predictions from previously encountered relational
structures. First, we show that this learner outperforms a naive, theory-based
learner on relational data derived from random- and Wikipedia-based systems
when experience with the environment is small. Next, we show how our
formalization of analogical similarity translates to the selection and
weighting of analogies. Finally, we combine the analogy- and theory-based
learners in a single nonparametric Bayesian model, and show that optimal
relational generalization transitions from relying on analogies to building a
theory of the novel system with increasing experience in it. Beyond predicting
unobserved interactions better than either baseline, this formalization gives a
computational-level perspective on the formation and abstraction of analogies
themselves.
