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Perspective Taking in Deep Reinforcement Learning Agents.

Aqeel Labash1, Jaan Aru1,2, Tambet Matiisen1

  • 1Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia.

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Summary
This summary is machine-generated.

Artificial agents can learn perspective-taking skills, crucial for social interaction, using artificial neural networks and reinforcement learning. This research advances human-like AI development for better communication.

Keywords:
artificial intelligencedeep reinforcement learningmulti-agentperspective takingtheory of mind

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Animal Behavior

Background:

  • Perspective taking, the ability to understand another agent's knowledge, is vital for social interactions like cooperation and communication.
  • This cognitive skill is observed in humans and other species, such as chimpanzees, highlighting its fundamental nature.
  • Developing artificial agents with perspective-taking capabilities is a key goal in advancing artificial intelligence.

Purpose of the Study:

  • To develop artificial agents capable of perspective taking, inspired by chimpanzee experiments.
  • To investigate the effectiveness of reinforcement learning in teaching artificial agents perspective-taking skills.
  • To compare the learning efficiency of allocentric versus egocentric information encoding for visual perception and motor actions.

Main Methods:

  • Implementation of a perspective-taking task modeled after chimpanzee behavioral studies.
  • Utilizing artificial neural networks trained via reinforcement learning to control agent behavior.
  • Comparative analysis of allocentric and egocentric information processing for visual and motor tasks.

Main Results:

  • Artificial agents demonstrated the ability to learn and pass simple perspective-taking tests.
  • Reinforcement learning proved effective in acquiring aspects of perspective-taking capabilities.
  • Findings provide insights into how information encoding (allocentric vs. egocentric) influences learning.

Conclusions:

  • Artificial agents can be trained to exhibit perspective-taking abilities.
  • This research contributes to building more human-like artificial intelligence.
  • Developing AI with perspective-taking enhances potential for more intuitive human-AI communication.