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Evolution of visually guided behavior in artificial agents.

Byron Boots1, Surajit Nundy, Dale Purves

  • 1Department of Neurobiology and Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.

Network (Bristol, England)
|April 25, 2007
PubMed
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This study shows that artificial agents evolved to act in virtual worlds learn to interpret visual information based on probable sources, not just raw data. This suggests vision evolved empirically to solve the inverse optics problem.

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Vision Science

Background:

  • Existing theories suggest human visual perception interprets probable sources of retinal images, rather than just stimulus features.
  • The 'inverse optics problem' highlights the challenge of reconstructing 3D scenes from 2D retinal images.

Purpose of the Study:

  • To investigate the empirical concept of vision by simulating agent evolution in virtual environments.
  • To determine if agents can learn to perceive based on behavioral success and statistical relationships in visual data.

Main Methods:

  • Autonomous agents evolved in virtual environments with success based solely on behavior.
  • Neural network control systems were analyzed for their incorporation of image-source-behavior statistical relationships.

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Main Results:

  • Evolved agents' visual responses improved as their neural networks integrated statistical relationships between images and appropriate behaviors.
  • Fitness increased with the incorporation of these learned statistical correlations, demonstrating an empirical approach to vision.

Conclusions:

  • An empirical strategy, learning from behavioral success, is effective for vision and solving the inverse optics problem.
  • Biological visual systems likely process information about the relationship between images and their probable sources.
  • This empirical processing may explain why human perception doesn't always align perfectly with physical reality.