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Adaptive search space pruning in complex strategic problems.

Ofra Amir1, Liron Tyomkin1, Yuval Hart2

  • 1Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel.

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Players use a "shutter" heuristic to efficiently search complex games, focusing on recent moves. While this strategy can lead to overlooking opponent threats, simulations show a narrow shutter is often optimal, even enhancing AI performance in limited computational scenarios.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Game Theory

Background:

  • Humans make complex strategic decisions despite limited cognitive resources.
  • Efficiently searching vast possibility spaces is crucial in strategic games.

Purpose of the Study:

  • To investigate human search strategies in k-in-a-row games.
  • To identify heuristics used for pruning search spaces.
  • To evaluate the effectiveness and trade-offs of these heuristics.

Main Methods:

  • Studied player search strategies in k-in-a-row games.
  • Employed computational simulations and analyzed behavioral data.
  • Varied parameters like shutter size, complexity, noise, and computational limits.

Main Results:

  • Players utilize scoring strategies and a "shutter" heuristic to prune search spaces.
  • A narrow shutter strategy is dominant across most simulated conditions.
  • The shutter heuristic improves deep learning network performance under computational constraints.

Conclusions:

  • The "shutter" heuristic is an adaptive strategy for navigating complex strategic games.
  • This heuristic offers a trade-off between search efficiency and overlooking opponent moves.
  • Findings suggest implications for both human cognition and AI development in strategic decision-making.