Lingying Wu
Papers
1
Total Citations
4
H-Index
1
About
Lingying Wu’s research focuses on multi-agent systems, cooperative patrol, and intelligent decision-making under constraints. Her most notable contribution is a learning method that determines the optimal activity cycle length (ACL) for agents operating in continuous cooperative patrol problems, particularly under battery limitations. This work addresses a critical challenge in multi-agent coordination: how agents can adapt their behavior based on environmental characteristics and the actions of other agents to maintain efficient, sustained patrol coverage. By enabling agents to dynamically adjust their cycles, Wu’s approach improves the robustness and longevity of autonomous systems in real-world applications such as surveillance, search-and-rescue, and environmental monitoring. Her 2019 paper on this topic has garnered 4 citations, reflecting its relevance in the emerging field of constrained multi-agent learning. Wu’s work is especially valuable for researchers exploring the intersection of reinforcement learning, resource management, and cooperative robotics, and it lays groundwork for more adaptive, energy-aware autonomous teams.
Research Focus
Key Achievements
Top Papers
- 1