Hybrid Neuro-Symbolic Pipelines for Explainable Decision-Making in Mission-Critical Systems
DOI:
https://doi.org/10.15662/IJEETR.2022.0406008Keywords:
Hybrid neuro-symbolic AI, explainable decision-making, mission-critical systems, symbolic reasoning, deep learning, transparency, safety constraints, XAI, knowledge representation, trustworthy AIAbstract
Mission-critical systems—such as autonomous vehicles, aerospace navigation, medical diagnostics, disaster-response robots, and critical infrastructure monitoring—demand AI models that are not only highly accurate but also robust, transparent, and explainable under extreme operational conditions. Traditional deep learning architectures often lack interpretability, while purely symbolic approaches struggle with scalability and uncertain environments. This research proposes a Hybrid Neuro-Symbolic Pipeline (HNSP) that unifies the perceptual strength of neural networks with the logical consistency of symbolic reasoning to achieve explainable, auditable, and verifiable decision-making in real-world mission-critical domains.
The proposed HNSP architecture integrates four major components: (1) Perception Layer, powered by deep neural networks for extracting high-dimensional features from multimodal inputs such as images, telemetry signals, sensor fusion outputs, or text reports; (2) Concept Extraction Layer, which converts continuous neural representations into discrete symbolic concepts using disentanglement techniques, attention-based concept activation vectors, or clustering-based symbolic abstraction; (3) Symbolic Reasoning Engine, which leverages rule-based logic, knowledge graphs, ontologies, and constraint satisfaction models to perform transparent reasoning aligned with domain-specific safety protocols; and (4) Explainability & Audit Module, which generates human-interpretable decision logs, causal reasoning traces, counterfactual justifications, and rule activation visualizations to ensure traceability and regulatory compliance.
The proposed pipeline addresses key challenges in mission-critical AI: reliability under distribution shifts, robustness against adversarial perturbations, traceability of inference, and incorporation of domain rules that cannot be learned solely from data. By enabling neural and symbolic components to interact bidirectionally, the system ensures that high-level reasoning can correct low-level perception errors and provide interpretable feedback. Furthermore, the architecture supports adaptive learning, where symbolic feedback constrains neural model updates, improving generalization and safety.
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