A Demonstration of Compositional, Hierarchical Interactive Task Learning

AAAI 2022 pp. 13203-13205

doi:10.1609/AAAI.V36I11.21728 /aaai/2022/mininger2022aaai-demonstration/

Abstract

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.

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Cite

Text

Mininger and Laird. "A Demonstration of Compositional, Hierarchical Interactive Task Learning." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21728

Markdown

[Mininger and Laird. "A Demonstration of Compositional, Hierarchical Interactive Task Learning." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/mininger2022aaai-demonstration/) doi:10.1609/AAAI.V36I11.21728

BibTeX

@inproceedings{mininger2022aaai-demonstration,
  title     = {{A Demonstration of Compositional, Hierarchical Interactive Task Learning}},
  author    = {Mininger, Aaron and Laird, John E.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13203-13205},
  doi       = {10.1609/AAAI.V36I11.21728},
  url       = {https://mlanthology.org/aaai/2022/mininger2022aaai-demonstration/}
}