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Projective simulation with generalization.

Alexey A Melnikov1,2, Adi Makmal3, Vedran Dunjko3

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Generalization is crucial for artificial intelligence agents to learn and handle data. This study introduces a novel machinery enabling projective simulation agents to generalize, which is essential for learning in complex environments.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Cognitive Science

Background:

  • Generalization is a key capability for intelligent agents to learn and adapt.
  • Projective simulation is a novel, physical approach to AI with demonstrated success in reinforcement learning.
  • Learning in certain environments is impossible without generalization capabilities.

Purpose of the Study:

  • To outline criteria for generalization in intelligent agents.
  • To present a dynamic and autonomous machinery for enabling projective simulation agents to generalize.
  • To analytically analyze the performance benefits of generalization for projective simulation agents.

Main Methods:

  • Developed a novel generalization machinery for projective simulation agents.
  • Utilized a dynamic and autonomous system architecture.
  • Conducted analytical performance analysis of the agent's learning process.

Main Results:

  • Demonstrated that learning can be impossible in extreme environments without generalization.
  • Showcased how the new machinery enables projective simulation agents to learn effectively.
  • Provided a full analytical analysis of the agent's performance gains through generalization.

Conclusions:

  • Generalization is a fundamental requirement for intelligent agents, particularly in challenging environments.
  • The presented machinery significantly enhances the learning capabilities of projective simulation agents.
  • The study provides a theoretical and analytical foundation for generalization in AI.