Hierarchical Neuro-Symbolic Constraint Propagation Framework for Real-Time Autonomous Decision Engineering in Multi-Agent Cyber-Physical Systems
Keywords:
neuro-symbolic artificial intelligence, constraint propagation optimization, cyber-physical systems engineering, multi-agent autonomous decision-making, graph neural network architectures, satisfiability modulo theories, real-time AI inference, formally verified machine learning, deep reinforcement learning integrationAbstract
The integration of neuro-symbolic reasoning with constraint propagation in multi-agent cyber-physical systems (CPS) presents critical scalability and inference latency challenges unresolved by existing architectures. This paper proposes a Hierarchical Neuro-Symbolic Constraint Propagation Framework (HNS-CPF) that couples deep graph neural networks with symbolic Satisfiability Modulo Theories (SMT) solvers to enable real-time, verifiable autonomous decision-making. The framework introduces a dual-layer abstraction mechanism: a perception-driven neural embedding layer and a logic-governed constraint resolution layer interconnected via a differentiable bridging module. Experimental evaluations conducted on heterogeneous robotic swarm environments and industrial IoT testbeds demonstrate a 38.7% reduction in decision latency and a 94.3% constraint satisfaction rate under dynamic adversarial perturbations. The proposed architecture establishes a reproducible baseline for formally verified AI-driven engineering systems operating under strict temporal and safety constraints.
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