ML Anthology

Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract)

Ruiqi He, Carlos G. Correa, Tom Griffiths, Mark K. Ho
AAAI 2024 pp. 23512-23513
doi:10.1609/AAAI.V38I21.30451 /aaai/2024/he2024aaai-structurally/

Abstract

How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can explain a wide range of simple planning problems by adding structure information to the task to facilitate planning in more complex tasks. The extended model was then applied to a more complex planning domain of spatial navigation. Our results suggest that our framework can correctly predict the navigation strategies of the majority of the participants in an online experiment.

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Text

He et al. "Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30451

Markdown

[He et al. "Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/he2024aaai-structurally/) doi:10.1609/AAAI.V38I21.30451

BibTeX

@inproceedings{he2024aaai-structurally,
  title     = {{Structurally Guided Task Decomposition in Spatial Navigation Tasks (Student Abstract)}},
  author    = {He, Ruiqi and Correa, Carlos G. and Griffiths, Tom and Ho, Mark K.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23512-23513},
  doi       = {10.1609/AAAI.V38I21.30451},
  url       = {https://mlanthology.org/aaai/2024/he2024aaai-structurally/}
}