Distributed AI Empowered by End-Edge-Cloud Computing: A Survey

In this study, researchers aim to improve the efficiency of energy trading in a multi-agent system by developing a hybrid mechanism that combines both centralized and decentralized approaches. The proposed method, called Hybrid_F_S, leverages the strengths of both approaches to achieve better social welfare performance while minimizing the time and energy overhead associated with decision-making.
To understand how Hybrid_F_S works, let’s first consider a simple analogy. Imagine a group of people working together to complete a task, where each person has their own set of skills and resources. Just like how a team leader can delegate tasks to individual team members, the decentralized approach in Hybrid_F_S delegates decision-making authority to individual agents. However, just as a team leader can also provide overall direction and guidance to the team, the centralized approach in Hybrid_F_S provides a high-level framework for decision-making, ensuring that all agents work towards a common goal.
The researchers evaluate the performance of Hybrid_F_S using several metrics, including time efficiency, energy efficiency, and social welfare. They find that Hybrid_F_S achieves better performance in all these areas compared to other approaches, making it an attractive option for improving energy trading efficiency.
Overall, the article provides a detailed analysis of a novel approach to energy trading, demonstrating its potential to improve efficiency and social welfare in a multi-agent system. By combining the strengths of both centralized and decentralized approaches, Hybrid_F_S offers a promising solution for optimizing energy trading decision-making processes.