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Backward reasoning through AND/OR trees to solve problems.

Jeroen Olieslagers1, Zahy Bnaya2, Yichen Li3

  • 1Center for Neural Science, New York University, New York, NY, United States.

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PubMed
Summary
This summary is machine-generated.

We introduce AND/OR trees, a new framework for backward reasoning in problem solving. This model effectively explains human actions in complex puzzles, offering insights into cognitive processes.

Keywords:
AND/OR treesRush Hourbackward reasoningproblem solvingsubgoals

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Psychology

Background:

  • Problem-solving is ubiquitous, yet a unifying framework for backward reasoning is lacking.
  • Existing models do not fully capture how humans decompose problems into subgoals and actions.

Purpose of the Study:

  • To introduce AND/OR trees as a novel representation for backward reasoning.
  • To investigate the predictive power of AND/OR trees on human problem-solving behavior.
  • To model human search strategies within AND/OR trees.

Main Methods:

  • Development of the AND/OR tree framework to represent problem-solving processes.
  • Conducting a study where participants solved deterministic, long-horizon puzzles.
  • Modeling participant search behavior using a single-parameter algorithm fitted to individual data.

Main Results:

  • AND/OR trees successfully explained a majority of participant actions during puzzle-solving.
  • The developed search algorithm captured key trends in the summary statistics of human play.
  • The model demonstrated psychological plausibility in representing backward reasoning.

Conclusions:

  • AND/OR trees offer a promising representational framework for backward reasoning.
  • This approach provides valuable insights into human cognitive strategies for problem-solving.
  • Further research can extend this framework to more complex problem domains.