2 citations · 2 across the 2 of their papers we have counts for
3 papers
TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
Haechang Kim, Hao Chen, Can Li +1
Explainable Reinforcement Learning (XRL) has emerged as a promising approach in improving the transparency of Reinforcement Learning (RL) agents. However, there remains a gap betwe…
OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
Hao Chen, Gonzalo Esteban Constante-Flores, Krishna Sri Ipsit Mantri +3
Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners…
FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis
Abdullah Khan, Rahul Nahar, Hao Chen +2
Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack inte…