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.

该论文提出了一种新的机制，使得 IMF 能够在运行时推广到不同形式的效用函数和意向优先级的变化，无需额外的训练，从而在客户意图和优先级频繁变化的实时网络中部署 IMF 并展示其高效性、可伸缩性和灵活性。