Multi Agent Framework
Multi-agent frameworks leverage the collaborative power of multiple artificial intelligence agents, often based on large language models (LLMs), to solve complex problems exceeding the capabilities of individual agents. Current research emphasizes efficient communication protocols to reduce computational costs and improve robustness against adversarial attacks, as well as the development of specialized agents for diverse tasks, including code generation, patent analysis, and urban mobility management. These frameworks are proving valuable for automating intricate workflows across various domains, improving efficiency and accuracy in areas like software development, financial analysis, and data reporting.
Papers
HEnRY: A Multi-Agent System Framework for Multi-Domain Contexts
Emmanuele Lacavalla, Shuyi Yang, Riccardo Crupi, Joseph E. Gonzalez
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners
Wen Zheng Terence Ng, Jianda Chen, Sinno Jialin Pan, Tianwei Zhang