We present a pilot study focused on creating flexible Hierarchical Task Networks which can leverage Reinforcement Learning to repair and adapt incomplete plans in the simulated rich domain of Minecraft. This paper presents an early evaluation of our algorithm using simulation for adaptive agents planning in a dynamic world. Our algorithm uses an hierarchical planner and can theoretically be used for any type of ”bot”. The main aim of our study is to create flexible knowledge-based planners for robots, which can leverage exploration and guide learning more efficiently by imparting structure using domain knowledge. Results from simulations indicate that a combined approach using both HTN and RL is more flexible than HTN alone and more efficient than RL alone.
Recent News
Ashok Goel: CogSci 2022
Sungeun An: Presentation at The 23rd International Conference on Artificial Intelligence in Education.
Sungeun An: Presentation at ITS (Intelligence Tutoring System) 2022 conference
Faces of Research: Meet Ashok Goel
XPrize has selected Georgia Tech’s Veritas team for the round of 10 teams in the Digital Learning Challenge