Neuro-Symbolic Agentic Swarms: A Hybrid Approach to Resilient Decision Making in Uncertain Edge Environments
DOI:
https://doi.org/10.15662/IJEETR.2026.0802057Keywords:
Neuro-Symbolic AI, Neuro-Symbolic AIEdge Computing, Distributed Intelligence, Autonomous Agents, Swarm Intelligence, Hybrid AI, Decision-Making, Internet of Things (IoT), Fault Tolerance, Uncertainty Handling.Abstract
Edge computing has become essential for real-time intelligent systems such as IoT networks, autonomous vehicles, and distributed sensor platforms, where decisions must be made under dynamic and resource-constrained conditions. However, traditional AI approaches struggle to handle uncertainty, incomplete data, and limited computational resources in such decentralized environments. This paper addresses the gap in existing research by overcoming the limitations of purely neural or purely symbolic systems in achieving robust and interpretable decision-making at the edge. The aim of this study is to develop a hybrid framework called Neuro-Symbolic Agentic Swarms for resilient and adaptive decision-making in uncertain edge environments. The proposed method integrates neural networks for perception and pattern recognition with symbolic reasoning for logical inference, deployed within a decentralized swarm of autonomous agents that collaborate through a hybrid coordination mechanism. The system is implemented using a layered architecture consisting of neural processing, symbolic inference, and agent coordination modules across distributed edge nodes. Experimental results demonstrate that the framework improves decision accuracy, reduces latency, and ensures fault tolerance even under noisy data conditions, node failures, and communication delays. The swarm-based collaboration further enhances scalability and robustness compared to conventional approaches. Overall, the proposed approach highlights the effectiveness of combining neuro-symbolic intelligence with agentic swarm systems, offering a scalable and reliable solution for real-time decision-making in complex edge computing environments.
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