Ruijia Zhou
Papers
1
Total Citations
3
H-Index
1
About
Ruijia Zhou is a researcher advancing the frontier of multi-agent decision-making under uncertainty, with a primary focus on interactive artificial intelligence and robotics. Her key research areas include partially observable Markov decision processes (POMDPs), multi-agent planning, and human-robot interaction. Zhou’s most notable contribution is her work on "Online Planning for Interactive-POMDPs using Nested Monte Carlo Tree Search" (2022), which addresses the critical challenge of enabling robots to make robust decisions in partially observed, non-cooperative multi-agent environments. By integrating nested Monte Carlo tree search with the Interactive-POMDP framework, she developed a scalable online planning method that allows robots to reason about other agents’ beliefs and intentions in real time—a fundamental capability for seamless human-robot collaboration. Although early in her career, with this work accumulating 3 citations, it represents a significant step toward practical deployment of interactive decision-making algorithms. Zhou’s research bridges theoretical planning models and real-world robotic applications, promising to enhance how autonomous systems operate in dynamic, socially complex settings.
Research Focus
Key Achievements
Top Papers
- 1