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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.
Summary
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.
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.
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