Generative Inferences Based on a Discriminative Bayesian Model of Relation Learning

Abstract

Bayesian Analogy with Relational Transformations (BART) is a discriminative model that can learn comparative relations from non-relational inputs (Lu, Chen & Holyoak, 2012). Here we show that BART can be extended to solve inference problems that require generation (rather than classification) of relation instances. BART can use its generative capacity to perform hypothetical reasoning, enabling it to make quasi-deductive transitive inferences (e.g., “If A is larger than B, and B is larger than C, is A larger than C?”). The extended model can also generate human-like instantiations of a learned relation (e.g., answering the question, “What is an animal that is smaller than a dog?”). These modeling results suggest that discriminative models, which take a primarily bottom-up approach to relation learning, are potentially capable of using their learned representations to make generative inferences.


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