Chien-Yu Chang
Papers
2
Total Citations
12
H-Index
2
About
Chien-Yu Chang is a pioneering researcher in cognitive robotics, specializing in the intersection of artificial intelligence, humanoid robot cognition, and biologically inspired learning algorithms. His work is distinguished by a novel approach that integrates psychological frameworks—particularly Daniel Kahneman’s “Thinking, Fast and Slow” dual-system model—into robotic decision-making processes. Chang’s most influential contributions include the development of a human-like thinking architecture for robots, demonstrated in his 2016 paper on robots that “think fast and slow” during ball-throwing tasks (6 citations). He further advanced this line of inquiry with a 2019 study introducing a deep belief network combined with inertia weight Particle Swarm Optimization, enabling humanoid robots to learn and adapt in pitching games (6 citations). These works collectively represent a significant step toward endowing machines with more intuitive, human-like cognition. Though his citation counts are modest, Chang’s research is notable for its conceptual boldness, bridging cognitive psychology and robotics in ways that inspire future explorations into artificial general intelligence and embodied cognition.
Research Focus
Key Achievements
Top Papers
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