A Demonstration of Compositional, Hierarchical Interactive Task Learning


  • Aaron Mininger University of Michigan
  • John E. Laird University of Michigan




Interactive Task Learning, Cognitive Robotics, Situated Interactive Instruction


We present a demonstration of the interactive task learning agent Rosie, where it learns the task of patrolling a simulated barracks environment through situated natural language instruction. In doing so, it builds a sizable task hierarchy composed of both innate and learned tasks, tasks formulated as achieving a goal or following a procedure, tasks with conditional branches and loops, and involving communicative and mental actions. Rosie is implemented in the Soar cognitive architecture, and represents tasks using a declarative task network which it compiles into procedural rules through chunking. This is key to allowing it to learn from a single training episode and generalize quickly.




How to Cite

Mininger, A., & Laird, J. E. (2022). A Demonstration of Compositional, Hierarchical Interactive Task Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13203-13205. https://doi.org/10.1609/aaai.v36i11.21728