Hugh Kwon
Papers
2
Total Citations
23
H-Index
2
About
Hugh Kwon is a researcher at the forefront of trustworthy and explainable artificial intelligence, with a primary focus on human-robot interactive decision-making. His work addresses a critical challenge in modern AI: building systems that are not only effective but also comprehensible and reliable for human users. Kwon’s major contributions center on developing interpretability enhancement techniques and neuro-symbolic hierarchical reinforcement learning frameworks that bridge the gap between complex machine learning models and human understanding. His most cited paper, “TDM: Trustworthy Decision-Making Via Interpretability Enhancement” (2021), has garnered 21 citations and lays foundational groundwork for establishing trust in autonomous systems by making their decision-making processes transparent. Additionally, his chapter on explainable neuro-symbolic hierarchical reinforcement learning (2021) further advances this mission by integrating symbolic reasoning with neural networks to create more interpretable AI agents. Kwon’s research is particularly impactful for students and researchers working at the intersection of AI safety, human-robot interaction, and explainable AI, as it provides practical methodologies for ensuring that autonomous systems can be trusted and understood by their human collaborators.
Research Focus
Key Achievements
Top Papers
- 1TDM: Trustworthy Decision-Making Via Interpretability Enhancement21 citations · 2021
- 2