Summary of Towards Adaptive Imfs — Generalization Of Utility Functions in Multi-agent Frameworks, by Kaushik Dey et al.
Towards Adaptive IMFs – Generalization of utility functions in Multi-Agent Frameworks
by Kaushik Dey, Satheesh K. Perepu, Abir Das, Pallab Dasgupta
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel mechanism for Intent Management Functions (IMFs) in future-generation networks. AI-based IMFs can handle conflicting intents by prioritizing global objectives based on utility functions and intent priorities. Previous works used Multi-Agent Reinforcement Learning (MARL) with AdHoc Teaming (AHT) approaches, but these require additional training to adapt to changing business situations. This paper presents a mechanism that generalizes different utility functions and intent priorities at runtime without requiring additional training. Results on a network emulator demonstrate the approach’s efficacy, scalability for new intents, outperforming existing techniques that require additional training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to manage many different requests or “intents” in a big network. It’s like juggling lots of balls at once! Some people have already worked on ways to make this process work better using artificial intelligence (AI). But, these solutions can be slow and need more practice to adapt to changing situations. This new paper proposes a way to improve the AI-based intent management function so it can quickly adjust to changes without needing more training. The results show that this approach is effective, efficient, and scalable. |
Keywords
» Artificial intelligence » Reinforcement learning
