Min Jo Kim
Papers
1
Total Citations
16
H-Index
1
About
Min Jo Kim’s research lies at the intersection of artificial intelligence, reinforcement learning, and adaptive behavior modeling. In her most-cited work, she introduced a novel dynamic priority-based action-selection mechanism that integrates reinforcement learning with a shortest-path-finding technique. This framework enables agents to learn sequential behaviors by defining behavioral motivation as a primitive node for action selection, allowing for more efficient and adaptive decision-making in complex environments. With 16 citations, this foundational paper has influenced subsequent studies in autonomous systems and cognitive robotics. Kim’s contributions are particularly notable for bridging reinforcement learning with biologically inspired action selection, offering a robust alternative to traditional static priority models. Her work has practical implications for developing intelligent agents capable of learning and adapting in real-time, making her a respected figure in the field of adaptive AI systems.
Research Focus
Key Achievements
Top Papers
- 1