Yongming Yang
Papers
2
Total Citations
56
H-Index
2
About
Yongming Yang is a leading researcher in multi-robot systems and distributed artificial intelligence, with a focus on reinforcement learning for dynamic task allocation. His seminal 2009 work on multi-robot task allocation for fire-disaster response introduced a reinforcement learning framework that enables robots to autonomously and efficiently coordinate in high-stakes, time-critical environments. This paper, with 37 citations, remains a foundational reference for applying learning-based methods to real-world disaster scenarios. Earlier, in 2007, Yang pioneered cooperative Q learning based on a blackboard architecture, a novel approach that allows multiple robots to share experiences and explore the learning space collectively. This work, cited 19 times, proposed three distinct Q-value update strategies, advancing the scalability and efficiency of multi-agent learning. Yang’s contributions are notable for bridging theoretical reinforcement learning with practical, distributed coordination challenges. His research has influenced subsequent work in swarm robotics, emergency response systems, and cooperative autonomous agents, making him a key figure in the development of intelligent, adaptive multi-robot teams.
Research Focus
Key Achievements
Top Papers
- 1
- 2Cooperative Q Learning Based on Blackboard Architecture19 citations · 2007