Principled integration of neural, symbolic, and probabilistic approaches

Develop a principled methodology to integrate neural network-based learning, symbolic reasoning, and probabilistic modeling into a unified neuro-symbolic AI framework, resolving the stated open challenge of how to combine these complementary approaches in a systematic manner.

Background

The paper argues that combining neural, symbolic, and probabilistic paradigms is promising for improving explainability and robustness in AI. However, current integrations are ad hoc and immature, lacking a principled framework. The authors explicitly note that determining how to integrate these approaches in a principled manner remains an open challenge.

A unified framework would guide the design of algorithms that opportunistically combine components across these paradigms and enable rigorous assessment of trade-offs and scaling behavior, which the authors identify as necessary for advancing neuro-symbolic AI.