Neuro-Symbolic Reasoning is a hybrid approach that combines neural networks with symbolic knowledge representations and logical reasoning mechanisms.

Neural networks provide robust pattern recognition and generalization, while symbolic systems enable explicit rules and logical inferences.

Typical methods integrate differentiable logical modules into neural architectures or use symbolic reasoners to support and explain network outputs.

This combination improves interpretability and enables more complex problem solving, such as through inference, planning, or deriving causes.

Challenges lie in efficient integration, scalability, and handling uncertainties in symbolic rules.