i-manager Publications

Synergistic Neuro-Symbolic Multi-Agent Architecture for Robust and Transparent Autonomous Systems

Rajesh Kumar Singh*, Apurv Solomon**

* SR Institute of Management and Technology, Lucknow, Uttar Pradesh, India.

** Feroze Gandhi Institute of Engineering and Technology, Uttar Pradesh, India.

Periodicity:July - December'2025
DOI : https://doi.org/10.26634/javr.3.2.22907

Abstract

This paper introduces a hybrid neuro-symbolic multi-agent architecture designed to strengthen the safety, interpretability, and reliability of autonomous systems. The proposed framework integrates neural perception and planning with symbolic reasoning, enabling agents to generate flexible action hypotheses while maintaining strict logical coherence through rule-based verification. The system is evaluated in robotic coordination and diagnostic environments, demonstrating improved task success, reduced unsafe behaviors, faster learning convergence, and clearer reasoning traces compared to neural-only and symbolic-only baselines. This approach offers a scalable foundation for next-generation autonomous systems requiring accountability, adaptability, and multi-agent consistency.

Keywords

Neuro-symbolic AI, Multi-Agent Systems, Autonomous Robotics, Reasoning, Hybrid Intelligence, Reinforcement Learning.

How to Cite this Article?

Singh, R. K., and Solomon, A. (2025). Synergistic Neuro-Symbolic Multi-Agent Architecture for Robust and Transparent Autonomous Systems. i-manager’s Journal on Augmented & Virtual Reality, 3(2), 29-37. https://doi.org/10.26634/javr.3.2.22907

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Synergistic Neuro-Symbolic Multi-Agent Architecture for Robust and Transparent Autonomous Systems

Abstract

Keywords

How to Cite this Article?

References

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