Chang-Shing Lee

About

Chang-Shing Lee is a pioneering researcher at the intersection of computational intelligence, human-robot interaction, and edutainment. His work centers on developing Fuzzy Markup Language (FML)-based agents that enable cooperative learning between humans and machines, with particular emphasis on the ancient game of Go as a testbed for artificial intelligence. Lee’s major contributions include the creation of Genetic Fuzzy Markup Language (GFML) frameworks and AI-FML agents that integrate fuzzy logic, neural networks, and evolutionary computation for real-world applications. His research has garnered over 80 citations across his most influential papers, with his 2020 work on FML-based reinforcement learning for human-robot cooperative edutainment leading at 16 citations. Notably, Lee has extended his frameworks to brain-computer interfaces (BCI) and emotional expression mechanisms, demonstrating the versatility of his soft-computing approaches. His 2017 ontology-based GFML agent for patent evaluation showcases the practical industrial applications of his methodologies. Through his Robotic Assistant Agent for AI-FML practice with AIoT applications, Lee continues to push boundaries in student-machine co-learning, establishing himself as a key figure in the evolution of intelligent, cooperative cybernetic systems.

Research Focus

Key Achievements

Top Papers

1. 1
   FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human-Robot Cooperative Edutainment

   16 citations · 2020
2. 2
   Soft-computing-based emotional expression mechanism for game of computer Go

   15 citations · 2012
3. 3
   Ontology-based GFML agent for patent technology requirement evaluation and recommendation

   11 citations · 2017
4. 4
   Robotic Assistant Agent for Student and Machine Co-Learning on AI-FML Practice with AIoT Application

   9 citations · 2021
5. 5
   AI-FML Agent for Robotic Game of Go and AIoT Real-World Co-Learning Applications

   9 citations · 2020
6. 6
   Adaptive Fuzzy Neural Agent for Human and Machine Co-learning

   7 citations · 2021
7. 7
   A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go

   5 citations · 2019
8. 8
   Human and Smart Machine Co-Learning: Brain-Computer Interaction at the 2017 IEEE International Conference on Systems, Man, and Cybernetics

   4 citations · 2018
9. 9
   PFML-based Semantic BCI Agent for Game of Go Learning and Prediction

   4 citations · 2019
10. 10
    FML-based Machine Learning Tool for Human Emotional Agent with BCI on Music Application

    3 citations · 2019

Key Collaborators

Contact & Links