English

Resource-rational Task Decomposition to Minimize Planning Costs

Artificial Intelligence 2020-07-29 v1 Machine Learning

Abstract

People often plan hierarchically. That is, rather than planning over a monolithic representation of a task, they decompose the task into simpler subtasks and then plan to accomplish those. Although much work explores how people decompose tasks, there is less analysis of why people decompose tasks in the way they do. Here, we address this question by formalizing task decomposition as a resource-rational representation problem. Specifically, we propose that people decompose tasks in a manner that facilitates efficient use of limited cognitive resources given the structure of the environment and their own planning algorithms. Using this model, we replicate several existing findings. Our account provides a normative explanation for how people identify subtasks as well as a framework for studying how people reason, plan, and act using resource-rational representations.

Keywords

Cite

@article{arxiv.2007.13862,
  title  = {Resource-rational Task Decomposition to Minimize Planning Costs},
  author = {Carlos G. Correa and Mark K. Ho and Fred Callaway and Thomas L. Griffiths},
  journal= {arXiv preprint arXiv:2007.13862},
  year   = {2020}
}

Comments

The first two authors contributed equally. To appear in Proceedings of the 42nd Annual Conference of the Cognitive Science Society (CogSci 2020)

R2 v1 2026-06-23T17:26:51.612Z