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About this paper
Justus Renkhoff, Ke Feng, Marc Meier-Doernberg, Alvaro Velasquez, Houbing Herbert Song
This survey comprehensively reviews Verification & Validation (V&V) and Testing & Evaluation (T&E) techniques for Neurosymbolic Artificial Intelligence (NSAI), illustrating how NSAI can enhance the explainability, robustness, and verifiability of sub-symbolic components. It identifies current V&V methods applicable to symbolic and sub-symbolic parts, analyzes the influence of neurosymbolic architectures, and pinpoints existing limitations and future research directions.
Key insights
- Neurosymbolic AI (NSAI) offers significant potential to ease V&V processes for sub-symbolic AI by using the symbolic component to provide transparency, explainability, and verifiable guarantees.
- The specific neurosymbolic architecture (e.g., "Reasoning for Learning," "Learning for Reasoning") profoundly influences which V&V aspects are enhanced and how methods can be applied, directly impacting robustness and interpretability.
- While V&V methods exist for individual symbolic and sub-symbolic components, dedicated frameworks and adjusted approaches are often required for thoroughly testing and validating integrated neurosymbolic systems, especially for complex or emerging architectures.
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