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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
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.
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.
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