Resource-rational Task Decomposition to Minimize Planning Costs
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jul 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 2.
9 papers.
- 2023Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automationcited by 57
- 2021Cognitive science as a source of forward and inverse models of human decisions for robotics and controlcited by 22
- 2022Humans decompose tasks by trading off utility and computational costcited by 18
- 2023ANPL: Towards Natural Programming with Interactive Decompositioncited by 13
- 2021Visual scoping operations for physical assemblycited by 2
- 2021A space of goals: the cognitive geometry of informationally bounded agentscited by 1
- 2024Enhancing Reasoning Capacity of SLM using Cognitive Enhancementcited by 1
- 2023Toward a normative theory of (self-)management by goal-settingcited by 0
- 2024Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planningcited by 0
- 2024Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning9 Sep 2024
- 2024Enhancing Reasoning Capacity of SLM using Cognitive Enhancement1 Apr 2024
- 2023Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automation4 Dec 2023
- 2023ANPL: Towards Natural Programming with Interactive Decomposition29 May 2023
- 2023Toward a normative theory of (self-)management by goal-setting6 Feb 2023
- 2022Humans decompose tasks by trading off utility and computational cost7 Nov 2022
- 2021A space of goals: the cognitive geometry of informationally bounded agents5 Nov 2021
- 2021Cognitive science as a source of forward and inverse models of human decisions for robotics and control1 Sep 2021
- 2021Visual scoping operations for physical assembly10 Jun 2021