Neuro-Symbolic AI for Autonomous Urban Decision-Making: A Framework for Resilient Smart Cities
Shubham Gupta, Kusumakumari Vanteru · 2025
The heterogeneous data streams generated from smart city infrastructures offer both potential and challenges to autonomous decision-making systems. While current deep learning approaches can perform amazing pattern recognition, they are essentially not interpretable, generally are not causal, and especially need lots of training data. This paper presents URBANSYM, a novel neuro-symbolic AI approach that unites the advantages of neural networks and the apprehension of autonomous urban decision-making. In the framework, heterogeneous urban data streams are processed using neural perception modules, and the symbolic reasoning layer includes reasoning of explicit causal process, counterfactual analysis, and incorporation of domain knowledge. On two critical, innovative city domains of interest, traffic management, energy distribution, and emergency response coordination, we demonstrate the effectiveness of our implementation of URBANSYM. The experiments on real-world datasets from three metropolitan areas show URBANSYM improves decision accuracy by 27%, response to anomalous events by 34%, and stakeholder trust ratings by 41% over pure deep learning methods. Additionally, the framework’s modular design enables knowledge transfer between urban contexts from 65% to 95% less training data when adapting to new cities. These results demonstrate that neuro-symbolic approaches can make a big difference in autonomous decision-making in complex urban environments with human interpretability and trust.