Daily Arxiv

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Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents

Created by
  • Haebom
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저자

Yue Zhong, Yongju Tong, Jiawen Kang, Minghui Dai, Hong-Ning Dai, Zhou Su, Dusit Niyato

개요

The paper introduces the Internet of Agents (IoA) architecture, which leverages interconnected AI agents for seamless discovery, communication, and collaborative reasoning. The architecture involves Wireless Agents (WAs) offloading compute-intensive tasks to Mobile Agents (MAs) or Fixed Agents (FAs). FAs can further offload tasks to Aerial Agents (AAs) using a two-tier optimization approach. The first tier uses a Stackelberg game for MAs and FAs to set resource prices and WAs to determine offloading ratios. The second tier employs a Double Dutch Auction for overloaded FAs to request resources from AAs. A diffusion-based Deep Reinforcement Learning algorithm is developed to solve the model, demonstrating superior task offloading performance.

시사점, 한계점

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시사점:
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Proposes a novel IoA architecture with a hierarchical offloading strategy.
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Employs a two-tier optimization approach combining a Stackelberg game and a Double Dutch Auction.
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Develops a diffusion-based Deep Reinforcement Learning algorithm for resource allocation.
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Demonstrates improved task offloading performance through numerical results.
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한계점:
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The paper does not explicitly detail the specific performance metrics used to evaluate the scheme.
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It's unclear if the model considers security or privacy concerns related to task offloading between different agent types.
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The computational cost of the proposed algorithm may be a factor in practical implementations.
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