Logical Rule Induction and Theory Learning Using Neural Theorem Proving

Understand

A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories.

• In this paper we present a new mechanism for logical theory acquisition which takes a set of observed facts and learns to extract from them a set of logical rules and a small set of core facts which together entail the observations.
• Our approach is neuro-symbolic in the sense that the rule pred- icates and core facts are given dense vector representations.
• The rules are applied to the core facts using a soft unification procedure to infer additional facts.