How can agents self-organize a reliable IoT network?
A Reliable Self-Organized Distributed Complex Network for Communication of Smart Agents
This paper explores decentralized communication in multi-agent systems, specifically for IoT networks, using a physics-inspired approach. Agents, trained with reinforcement learning and guided by a Hamiltonian cost function, adjust their transmission radii to optimize network connectivity and energy consumption. The decentralized nature of the system makes it robust and adaptable to dynamic environments, including node additions, removals, and the presence of obstacles. The collaboration between agents, where they evaluate the impact of their actions on neighbors, further enhances the system's performance.
Key points for LLM-based multi-agent systems: decentralized control, reinforcement learning for agent training, physics-inspired cost functions (Hamiltonian), focus on robustness and adaptability in dynamic environments, and inter-agent collaboration for optimized performance.