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）是未来网络的关键组成部分。近年来，已有研究基于人工智能的IMF，可通过预先定义的效用函数及竞争意图的优先级排序，处理冲突意图并优先实现全局目标。部分早期工作采用基于多智能体强化学习（MARL）与临时团队协作（AHT）的方法，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需具备对业务情境的灵活性。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定义亦可能发生改变。本文提出一种新型机制，使IMF能够在无需额外训练的情况下，运行时泛化至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实时网络中。网络仿真器上的实验结果验证了该方法的有效性、对新意图的可扩展性，并在实现相同灵活度的前提下，优于需要额外训练的现有技术，从而降低开销、提升效率与适应性。