To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline.

为了有效表达和满足网络应用的需求，我们提出了一种基于人工智能的框架，用于意图识别和翻译。该框架基于新兴通信实现意图识别，应用通过通信消息表达他们的抽象化用户体验意图，网络学习如何解释这些通信消息并映射到网络能力，从而保证所需的服务质量。模拟结果显示，该方法胜过自学习分片方法和其他基准方法，接近完美知识基准。

意图分析和通过新兴交流进行翻译