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Much of human learning and inference can be framed within the computationalproblem of relational generalization. In this project, we propose a Bayesianmodel that generalizes relational knowledge to novel environments byanalogically weighting predictions from previously encountered relationalstructures. First, we show that this learner outperforms a naive, theory-basedlearner on relational data derived from random- and Wikipedia-based systemswhen experience with the environment is small. Next, we show how ourformalization of analogical similarity translates to the selection andweighting of analogies. Finally, we combine the analogy- and theory-basedlearners in a single nonparametric Bayesian model, and show that optimalrelational generalization transitions from relying on analogies to building atheory of the novel system with increasing experience in it. Beyond predictingunobserved interactions better than either baseline, this formalization gives acomputational-level perspective on the formation and abstraction of analogiesthemselves.

Analogy as Nonparametric Bayesian Inference over Relational Systems