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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2405.07621 |
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| _version_ | 1866916245386821632 |
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| author | Dey, Kaushik Perepu, Satheesh K. Das, Abir Dasgupta, Pallab |
| author_facet | Dey, Kaushik Perepu, Satheesh K. Das, Abir Dasgupta, Pallab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks Dey, Kaushik Perepu, Satheesh K. Das, Abir Dasgupta, Pallab Machine Learning Artificial Intelligence 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. |
| title | Towards Adaptive IMFs -- Generalization of utility functions in Multi-Agent Frameworks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.07621 |