Jack-Antoine Charles
Papers
1
Total Citations
6
H-Index
1
About
Jack-Antoine Charles is a rising researcher at the intersection of human-agent interaction and crowdsourced machine learning. His work focuses on developing adaptive systems that can intelligently respond to the unpredictable behavior of human operators in high-stakes environments. In his most-cited paper, "Human-Agent Interaction Model Learning based on Crowdsourcing" (2018), Charles pioneered a novel framework that leverages crowdsourcing to model and anticipate human decision-making, enabling automated systems to dynamically adjust their behavior to reduce failure risk. This approach has significant implications for mission-critical applications, from autonomous vehicles to collaborative robotics. With 6 citations, his work is gaining traction as a foundational contribution to human-in-the-loop AI. Charles’s research addresses a pressing challenge: how to design systems that are not only technically robust but also socially intelligent, capable of reading and reacting to human unpredictability. His innovative use of crowdsourced data to train interaction models marks a notable achievement, offering a scalable path toward safer, more responsive human-agent teams.
Research Focus
Key Achievements
Top Papers
- 1Human-Agent Interaction Model Learning based on Crowdsourcing6 citations · 2018