Adaptive planning depth in human problem-solving

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them is not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources-namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. These results support the view of problem-solving as a bounded rational process, which adapts costly cognitive resources to task demands.

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Publication Details

Journal
Royal Society Open Science
Published
2025-04-01
DOI
https://doi.org/10.1098/rsos.241161
Citations
4
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
9.92

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article

Adaptive planning depth in human problem-solving

Mattia Eluchans, Gian Luca Lancia, Giovanni Pezzulo, Antonella Maselli et al.
4 citations
Royal Society Open Science
AI-based Problem Solving and Planning
9.92
article

Adaptive planning depth in human problem-solving

Mattia Eluchans, Gian Luca Lancia, Giovanni Pezzulo, Antonella Maselli, Jeremy Gordon, Marco D’Alessandro
article en
4 citations

Abstract

We humans are capable of solving challenging planning problems, but the range of adaptive strategies that we use to address them is not yet fully characterized. Here, we designed a series of problem-solving tasks that require planning at different depths. After systematically comparing the performance of participants and planning models, we found that when facing problems that require planning to a certain number of subgoals (from 1 to 8), participants make an adaptive use of their cognitive resources-namely, they tend to select an initial plan having the minimum required depth, rather than selecting the same depth for all problems. These results support the view of problem-solving as a bounded rational process, which adapts costly cognitive resources to task demands.

Royal Society Open ScienceVol. 12(4)
University of Messina (IT), National Academies of Sciences, Engineering, and Medicine (US), Institute of Cognitive Sciences and Technologies (IT), Sapienza University of Rome (IT), University of California, Berkeley (US)
H2020 European Research Council
Sustainable cities and communities
Openalex Percentile: Top 2%
AI-based Problem Solving and Planning
9.92
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Adaptive planning depth in human problem-solving — Mattia Eluchans, Gian Luca Lancia, et al. · Royal Society Open Science (2025) | TGRS Research Map | TGRS