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Artificial intelligence (AI) research initially aimed for human-like behavior. This study explores if current AI, focusing on dynamic decision-making via instance-based learning theory, achieves this goal, identifying gaps for future human-like AI development.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Psychology

Background:

  • Early artificial intelligence (AI) pursued human-like behavior, aiming for indistinguishable performance.
  • Current AI often prioritizes outperforming humans in specific tasks over replicating human cognition.
  • A divergence exists between AI's initial goals and its current trajectory.

Purpose of the Study:

  • To investigate whether computational algorithms have achieved the initial AI goal of exhibiting human-like behavior.
  • To explore the question from the perspective of computational cognitive science, focusing on dynamic decision-making.
  • To assess the current state of AI in emulating human decision processes.

Main Methods:

  • Presents a general cognitive algorithm designed to emulate human decision-making in dynamic environments.
  • Utilizes instance-based learning theory (IBLT) to structure the discussion of evidence.
  • Analyzes existing research to evaluate the human-likeness of current AI decision-making mechanisms.

Main Results:

  • Identifies evidence supporting the human-likeness of certain AI decision-making mechanisms based on IBLT cognitive steps.
  • Highlights significant research gaps hindering the development of higher-fidelity computational models of human decision processes.
  • Demonstrates that while some progress has been made, AI has not fully achieved the initial goal of human-like behavior.

Conclusions:

  • Current computational algorithms show partial human-likeness in dynamic decision-making, particularly when guided by theories like IBLT.
  • Significant research is needed to bridge the gap between current AI capabilities and true human cognitive emulation.
  • Future AI development should focus on advancing algorithms that exhibit human-like behavior to better support human decision-making.