# Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks

arXiv: https://arxiv.org/abs/2405.07621 · HTML: https://mlsanity.com/paper/2405.07621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 7 of 7)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Planning with Goal-Conditioned Policies](https://mlsanity.com/paper/1911.08453/) (2019)
- [M3RL: Mind-aware Multi-agent Management Reinforcement Learning](https://mlsanity.com/paper/1810.00147/) (2018)
- [Intent-driven autonomous network and service management in future cellular networks: A structured literature review](https://mlsanity.com/paper/2108.04560/) (2021)
- [Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs](https://mlsanity.com/paper/2310.17416/) (2023)

## Abstract (arXiv)

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.