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Action Selection in Everyday Activities: The Opportunistic Planning Model.

Petra Wenzl1, Holger Schultheis2

  • 1Institute for Artificial Intelligence, University of Bremen.

Cognitive Science
|April 24, 2024
PubMed
Summary

People prefer specific action orders in everyday tasks due to bounded rationality, aiming to minimize effort. The Opportunistic Planning Model explains this by leveraging spatial environments and stepwise-optimal strategies.

Keywords:
Action selectionOpportunistic planningSpatial cognition

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Action selection in well-defined tasks is well-studied.
  • Understanding decision-making in ill-defined, everyday tasks remains limited.
  • Everyday tasks often present multiple valid solution paths.

Purpose of the Study:

  • Investigate action selection strategies in ill-defined everyday tasks.
  • Explain observed human preferences in task sequencing.
  • Propose and validate a cognitive model for opportunistic planning.

Main Methods:

  • Developed the Opportunistic Planning Model (OPM).
  • Modeled action selection based on bounded rationality principles.
  • Evaluated OPM's generalization capabilities on new everyday tasks.

Main Results:

  • Human action selection in ill-defined tasks shows specific, non-random preferences.
  • Preferences are driven by minimizing physical and cognitive effort.
  • OPM successfully generalizes to new tasks, outperforming machine learning models in generalization.

Conclusions:

  • Bounded rationality influences everyday task planning through effort minimization.
  • Spatial environment properties and stepwise-optimal strategies are key.
  • The Opportunistic Planning Model offers a robust explanation for human action selection in ill-defined problems.