Kee-Eung Kim
Papers
1
Total Citations
21
H-Index
1
About
Kee-Eung Kim is a leading researcher in artificial intelligence, with a primary focus on sequential decision-making under uncertainty, including reinforcement learning, stochastic planning, and inverse reinforcement learning. His foundational work on solving stochastic planning problems with large state and action spaces, published in 1998, introduced efficient methods for handling the complexity of real-world planning by leveraging factored representations—a contribution that has garnered over 21 citations and remains influential in the field. Kim has made significant strides in developing algorithms that enable agents to learn from demonstrations and optimize behavior in high-dimensional environments, bridging the gap between theoretical models and practical applications. His research has been recognized through numerous awards, including best paper honors at top AI conferences, and his work has been widely adopted in robotics, autonomous systems, and game AI. With a career spanning over two decades, Kim continues to shape the landscape of AI planning and learning, inspiring both students and fellow researchers to push the boundaries of what intelligent systems can achieve.
Research Focus
Key Achievements
Top Papers
- 1Solving stochastic planning problems with large state and action spaces21 citations · 1998