Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Cyber-Physical Systems

Background:

• Neuro-Symbolic reasoning (NeSy) integrates neural learning and symbolic reasoning.
• Existing NeSy systems often lack causal reasoning, cognitive autonomy, and self-evolution capabilities for open-world environments.
• Current NeSy approaches struggle with causal cognitive complexities.

Purpose of the Study:

• To propose Self-Evolving Neuro-Symbolic Autonomy (SENSA) for autonomous cognitive decision-making.
• To address limitations in current NeSy research regarding causal reasoning and adaptability.
• To achieve robust AI performance in dynamic, open-world settings.

Main Methods:

• Developed neural-symbolic bidirectional self-alignment for real-time adaptation and robustness.
• Implemented causal-driven self-evolution using causal reasoning and counterfactual analysis.
• Designed hierarchical resilience planning for performance maintenance under disruptive conditions.

Main Results:

• SENSA demonstrates superior performance over state-of-the-art baselines across eight benchmark datasets.
• The proposed system achieves causal reasoning, cognitive autonomy, and adaptive self-evolution.
• Theoretical proofs confirm SENSA's ability to approximate ground-truth causal reasoning and autonomy.

Conclusions:

• SENSA effectively bridges neural adaptability and symbolic rigor, creating an 'industrial brain'.
• The system enables robust, autonomous cognitive decision-making in Cyber-Physical Systems.
• SENSA represents a significant advancement in developing resilient and self-evolving AI.