4 citations · 14 across the 11 of their papers we have counts for
5 papers · 1 filter
COOP: Defining, Observing, and Repairing Cooperation in LLM Multi-Agent Systems
Hanqing Yang, Narjes Nourzad, Shiyu Chen +3
Many complex tasks require extended effort, diverse capabilities, or coordinated actions beyond what a single agent can provide. However, simply adding more agents does not guarant…
DIG to Heal: Scaling General-purpose Agent Collaboration via Explainable Dynamic Decision Paths
Hanqing Yang, Hyungwoo Lee, Yuhang Yao +4
The increasingly popular agentic AI paradigm promises to harness the power of multiple, general-purpose large language model (LLM) agents to collaboratively complete complex tasks.…
The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs
Jingdi Chen, Hanqing Yang, Zongjun Liu +1
Multi-agent sequential decision-making powers many real-world systems, from autonomous vehicles and robotics to collaborative AI assistants. In dynamic, partially observable enviro…
LLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning
Hanqing Yang, Jingdi Chen, Marie Siew +2
Developing intelligent agents for long-term cooperation in dynamic open-world scenarios is a major challenge in multi-agent systems. Traditional Multi-agent Reinforcement Learning…
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning
Jingdi Chen, Tian Lan, Carlee Joe-Wong
Communication is crucial for solving cooperative Multi-Agent Reinforcement Learning tasks in partially observable Markov Decision Processes. Existing works often rely on black-box…