Intent Management Function (IMF) is an integral part of future-generation networks. In recent years, there has been some work on AI-based IMFs that can handle conflicting intents and prioritize the global objective based on apriori definition of the utility function and accorded priorities for competing intents. Some of the earlier works use Multi-Agent Reinforcement Learning (MARL) techniques with AdHoc Teaming (AHT) approaches for efficient conflict handling in IMF. However, the success of such frameworks in real-life scenarios requires them to be flexible to business situations. The intent priorities can change and the utility function, which measures the extent of intent fulfilment, may also vary in definition. This paper proposes a novel mechanism whereby the IMF can generalize to different forms of utility functions and change of intent priorities at run-time without additional training. Such generalization ability, without additional training requirements, would help to deploy IMF in live networks where customer intents and priorities change frequently. Results on the network emulator demonstrate the efficacy of the approach, scalability for new intents, outperforming existing techniques that require additional training to achieve the same degree of flexibility thereby saving cost, and increasing efficiency and adaptability.

翻译：意图管理功能（IMF）是未来网络的核心组成部分。近年来，基于人工智能的IMF研究取得进展，这些IMF能够处理冲突意图，并根据效用函数的先验定义及竞争意图的优先顺序来优先实现全局目标。早期部分工作采用多智能体强化学习（MARL）与即兴团队协作（AHT）技术，以高效处理IMF中的冲突。然而，此类框架在实际场景中的成功应用要求其具备对商业场景的灵活性：意图优先级可能动态变化，而衡量意图实现程度的效用函数也可能在定义上发生改变。本文提出一种新型机制，使IMF能够在运行时无需额外训练即可泛化到不同形式的效用函数及意图优先级变化。这种无需额外训练的泛化能力有助于将IMF部署于客户意图与优先级频繁变化的现网中。在网络仿真器上的实验结果表明，该方法在有效性、新意图可扩展性方面均优于现有技术——传统方法需额外训练才能达到同等灵活性，而本方法通过节省成本、提升效率与适应性展现出显著优势。