Generating new concepts with hybrid neuro-symbolic models

Understand

Human conceptual knowledge supports the ability to generate novel yet highly structured concepts, and the form of this conceptual knowledge is of great interest to cognitive scientists.

• One tradition has emphasized structured knowledge, viewing concepts as embedded in intuitive theories or organized in complex symbolic knowledge structures.
• A second tradition has emphasized statistical knowledge, viewing conceptual knowledge as an emerging from the rich correlational structure captured by training neural networks and other statistical models.
• In this paper, we explore a synthesis of these two traditions through a novel neuro-symbolic model for generating new concepts.