Neuro-symbolic robustness pipelines operate by having a neural perception module convert raw sensory inputs into probabilistic concept embeddings, which are then injected into a symbolic knowledge store and a logical reasoner or planner. The reasoner combines symbolic priors with neural evidence to propose candidate decisions, which are scrutinized by a verifier enforcing formal constraints. Verified outputs are released as actions, while violations trigger a feedback loop that supplies explanatory traces for explanation-based fine-tuning.

Authors

• Person: Not available Organization: Springer
  A Comprehensive Review of Neuro-symbolic AI for Robustness ...

Sources

• A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer via serper

Referenced by nodes (1)

• neural perception module concept