TRIDENT: A Trusted Neuro-Symbolic Framework for Autonomous Systems in Unstructured Environments
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I1P111Keywords:
Neuro-Symbolic AI, Autonomous Systems, Federated Learning, Explainable AI (XAI), Trust Metrics, RoboticsAbstract
Autonomous systems operating in unstructured environments face the dual challenges of robust decision-making under ambiguity and the need for verifiable, explainable behavior. Purely data-driven deep learning approaches excel at perceptual tasks but often lack the ability to reason logically or generalize to out-of-distribution scenarios, leading to unpredictable failures. Conversely, traditional symbolic logic systems are interpretable but brittle in the face of noisy, high-dimensional sensory input. This paper introduces TRIDENT (Trusted Reasoning and Integration for Intelligent Decentralized Navigation), a novel neuro- symbolic framework that synergistically fuses deep learning with symbolic logic to enhance autonomy. TRIDENT’s architecture consists of a neural perception module that grounds raw sensor data into a symbolic knowledge base, and a logical reasoning engine that uses this knowledge to perform robust, explainable planning. To enable collaborative learning across decentralized fleets of autonomous agents, we embed TRIDENT within a Trust-Metric-based Federated Learning (TMFL) scheme. TMFL ensures the integrity and accountability of the shared model by dynamically weighting contributions from each agent based on their performance and behavioral consistency. Furthermore, we introduce a quantitative framework to navigate the critical trade-off between the system’s operational performance and its explainability. By controlling the degree of symbolic oversight on the neural subsystem, we can generate a Pareto frontier of policies, allowing for principled selection based on mission-specific safety and transparency requirements. We validate TRIDENT in complex simulated autonomous driving scenarios, demonstrating superior zero-shot generalization, resilience to adversarial participants in the federated network, and a practical methodology for producing high-performance yet scrutable autonomous agents
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