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Related Experiment Videos

Artificial evolution: a new path for artificial intelligence?

P Husbands1, I Harvey, D Cliff

  • 1School of Cognitive and Computing Sciences, University of Sussex, Brighton, United Kingdom. philh@cogs.susx.ac.uk

Brain and Cognition
|June 1, 1997
PubMed
Summary
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Artificial evolution offers a novel approach to developing control systems for autonomous robots. This study demonstrates concurrent evolution of robot control networks and sensor designs for enhanced autonomy.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Autonomous robots require sophisticated control systems.
  • Traditional control system development can be complex and time-consuming.
  • Artificial evolution presents a new paradigm for designing robot controllers.

Purpose of the Study:

  • To explain the artificial evolution approach for robot control.
  • To present a case study on evolving control networks and sensor morphology.
  • To discuss broader implications for theoretical biology.

Main Methods:

  • Utilized artificial evolution techniques.
  • Focused on concurrent evolution of control networks and visual sensor morphologies.
  • Employed a mobile robot as a case study.

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Main Results:

  • Successfully demonstrated the concurrent evolution of control and sensor systems.
  • Illustrated the practical application of artificial evolution in robotics.
  • Provided insights into the potential of evolutionary simulations in theoretical biology.

Conclusions:

  • Artificial evolution is a viable and promising approach for autonomous robot control system development.
  • The case study highlights the effectiveness of evolving both control and morphology.
  • Evolutionary simulations offer a valuable tool for theoretical biology research.