# Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network

arXiv: https://arxiv.org/abs/2210.06649 · HTML: https://mlsanity.com/paper/2210.06649/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2107.02794/) (2021)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [Edge Continual Learning for Dynamic Digital Twins over Wireless Networks](https://mlsanity.com/paper/2204.04795/) (2022)
- [Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication](https://mlsanity.com/paper/2205.10768/) (2022)
- [Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicing](https://mlsanity.com/paper/2201.08985/) (2022)
- [An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery](https://mlsanity.com/paper/2201.10822/) (2022)

## Built on by (most cited first, 8 of 8)

- [Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case](https://mlsanity.com/paper/2307.12903/) (2023)
- [ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins](https://mlsanity.com/paper/2501.08561/) (2025)
- [TEFL: Turbo Explainable Federated Learning for 6G Trustworthy Zero-Touch Network Slicing](https://mlsanity.com/paper/2210.10147/) (2022)
- [Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid](https://mlsanity.com/paper/2306.07993/) (2023)
- [Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing](https://mlsanity.com/paper/2307.09494/) (2023)
- [AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space](https://mlsanity.com/paper/2506.22487/) (2025)
- [Governable AI: Provable Safety Under Extreme Threat Models](https://mlsanity.com/paper/2508.20411/) (2025)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)

## Abstract (arXiv)

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. In this paper, a novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multi-arm bandits-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. To address the challenges of extensible, modular, and stateless management functions in ZSM, the proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) an implicit learner that acts as an unconscious learner in physical space, and 2) an explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.