Neuro-Symbolic Machine Learning Framework for Adaptive Decision Intelligence in Distributed Smart Systems

Abstract

A new framework for Neuro-Symbolic Machine Learning (NS-ML) to enhance the adaptive decision intelligence of distributed smart systems is introduced. As smart environments become more complex, the transparency and reasoning of traditional black-box deep learning models in uncertain circumstances are unsatisfactory. Researchers address this deficit with a hybrid approach that combines neural networks (NNs), which excel at capturing perceptual patterns, and symbolic logic engines (SLEs), which excel at making decisions structured and explainable. The model uses both connectionist learning and formal knowledge representation to achieve high adaptability in a dynamic environment. The experimental study uses a well-defined set of 364 real-time sensor telemetry and operational event logs collected from an experimental smart grid infrastructure testbed. Researchers used Python to implement the framework and PyTorch for the neural parts, while the CLINGO engine handled the symbolic reasoning. The results show that our hybrid method is much more accurate in decision-making and logical consistency than using a neural network alone, especially when operational conditions vary. The framework offers a solid base for the development of self-optimising distributed systems, which must not only be capable of high-speed processing but also give assurance of decision-making logic, and is therefore suitable for modern industrial automation and smart city buildings.