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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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When is Psychology Research Useful in Artificial Intelligence? A Case for Reducing Computational Complexity in

Sébastien Hélie1, Zygmunt Pizlo2

  • 1Department of Psychological Sciences, Purdue University.

Topics in Cognitive Science
|September 1, 2021
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Summary

Humans solve complex problems efficiently by creating mental representations, not by exhaustive search. Understanding these heuristics can improve artificial intelligence algorithms for faster computation.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Complexity

Background:

  • Human problem solving is often modeled as a search within a vast problem space.
  • The computational complexity of real-world problems far exceeds human or current computer capabilities for exhaustive search.
  • Despite this, humans solve complex problems rapidly and efficiently daily.

Purpose of the Study:

  • To investigate the cognitive mechanisms behind efficient human problem solving.
  • To explore how problem representation influences solution strategies.
  • To inform the development of more efficient artificial intelligence algorithms.

Main Methods:

  • Analysis of human problem-solving strategies.
  • Review of cognitive capacity limitations and cost-benefit analyses in decision-making.
  • Conceptual framework linking human heuristics to AI.

Main Results:

  • Humans construct and solve internal problem representations rather than the objective problem.
  • Cognitive capacity limits and cost-benefit calculations shape these representations and solution processes.
  • Heuristics play a crucial role in simplifying complex problems.

Conclusions:

  • Understanding human problem representation and heuristic strategies is key to efficient problem solving.
  • This understanding can guide the creation of simpler, faster AI algorithms.
  • Applying human-like heuristics can reduce computational complexity and time in AI.