Intent-based network (IBN) is a promising solution to automate network operation and management. IBN aims to offer human-tailored network interaction, allowing the network to communicate in a way that aligns with the network users' language, rather than requiring the network users to understand the technical language of the network/devices. Nowadays, different applications interact with the network, each with its own specialized needs and domain language. Creating semantic languages (i.e., ontology-based languages) and associating them with each application to facilitate intent translation lacks technical expertise and is neither practical nor scalable. To tackle the aforementioned problem, we propose a context-aware AI framework that utilizes machine reasoning (MR), retrieval augmented generation (RAG), and generative AI technologies to interpret intents from different applications and generate structured network intents. The proposed framework allows for generalized/domain-specific intent expression and overcomes the drawbacks of large language models (LLMs) and vanilla-RAG framework. The experimental results show that our proposed intent-RAG framework outperforms the LLM and vanilla-RAG framework in intent translation.

翻译：基于意图的网络（IBN）是实现网络运维自动化的有前景方案。IBN旨在提供面向人类习惯的网络交互方式，使网络能够以与用户语言一致的方式进行通信，而非要求用户理解网络/设备的技术语言。当前，不同应用程序以各自特有的需求和领域语言与网络交互。为每个应用创建语义语言（即基于本体的语言）并关联以促进意图翻译，既缺乏技术专业性，也不具备实用性和可扩展性。为解决上述问题，我们提出一种上下文感知的AI框架，该框架利用机器推理（MR）、检索增强生成（RAG）和生成式AI技术，从不同应用中解析意图并生成结构化网络意图。所提框架支持通用/领域特定的意图表达，克服了大语言模型（LLM）和基础RAG框架的缺陷。实验结果表明，我们提出的意图-RAG框架在意图翻译任务上优于LLM和基础RAG框架。