Communication networks are becoming increasingly complex toward 6G. Manual management is no longer an option for network operators. Network automation has been widely discussed in the networking community, and it is a sensible means to manage the complex communication network. Deep learning models developed to enable network automation for given operation practices have the limitations of lack of explainability and inapplicability across different networks and/or network settings. To tackle the above issues, in this article we propose a new knowledge-powered framework that provides a human-understandable explainable artificial intelligence (XAI) agent for network automation. A case study of path selection is developed to demonstrate the feasibility of the proposed framework. Research on network automation is still in its infancy. Therefore, at the end of this article, we provide a list of challenges and open issues that can guide further research in this important area.

中文翻译：

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面向 6G 网络自动化的知识驱动可解释人工智能

通信网络正朝着 6G 方向变得越来越复杂。手动管理不再是网络运营商的选择。网络自动化已在网络界广泛讨论，是管理复杂通信网络的明智手段。为实现给定操作实践的网络自动化而开发的深度学习模型具有缺乏可解释性和不适用于不同网络和/或网络设置的局限性。为了解决上述问题，在本文中，我们提出了一个新的知识驱动框架，该框架为网络自动化提供了人类可理解的可解释人工智能 (XAI) 代理。开发了一个路径选择的案例研究来证明所提出框架的可行性。网络自动化的研究仍处于起步阶段。所以，