Designing Hybrid Artificial Intelligence Systems: Integrating Symbolic Reasoning and Deep Learning for Real-Time, Context-Aware Decision Making in Complex Environments

Abstract

Hybrid Artificial intelligence systems which are hybrids of deep learning and symbolic reasoning, are emerging as a potent solution for real time context aware decision making in complex environments. In this research, a new hybrid AI architecture was developed which uses convolutional neural networks (CNNs) for perception and applies rule based inference through a Prolog symbolic reasoning engine. This is achieved through a middleware layer that translates neural outputs to logical predicates, allowing for the dynamic interaction between perception and reasoning. The proposed system is evaluated across eight autonomous driving scenarios in terms of decision accuracy, its contextual fit, reaction time and interpretability. The results demonstrate that the hybrid model outperforms standalone symbolic or Deep Learning systems in all the evaluated metrics, improving the generalization, reducing the error in ambiguous cases and being more transparent. The study shows that such integration can not only close the interpretability gap in black box models but also improve system adaptability in safety critical tasks. This paves the way for the hybrid paradigm to be a viable path towards developing intelligent systems similar to the human way of thinking and trustworthy decision making.