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Evolving control metabolisms for a robot.

J Ziegler1, W Banzhaf

  • 1Department of Computer Science, University of Dortmund, 44221 Dortmund, Germany. ziegler.banzhaf@ls11.cs.uni-dortmund.de

Artificial Life
|October 3, 2001
PubMed
Summary
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This study introduces a novel programming method for artificial chemistries, enabling robots to adapt and navigate by evolving metabolic pathways. This approach mimics biological systems like motile bacteria for efficient information processing.

Area of Science:

  • Artificial intelligence
  • Computational chemistry
  • Robotics

Background:

  • Artificial chemistries offer a novel paradigm for computation.
  • System dynamics can be harnessed for information processing.
  • Metabolisms can serve as control programs in artificial systems.

Purpose of the Study:

  • To demonstrate a new method for programming artificial chemistries.
  • To utilize system dynamics for information processing in robots.
  • To achieve adaptation of metabolic pathways and exteroceptor selection for robot navigation.

Main Methods:

  • Programming artificial chemistries using system dynamics.
  • Evolving metabolisms as control programs for a small robot.
  • Adapting internal metabolic pathways and selecting relevant exteroceptors.

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Main Results:

  • Artificial chemistry evolved efficient information-processing pathways.
  • Pathways were optimized for robot navigation tasks.
  • Adaptation of internal pathways and exteroceptor selection were achieved.

Conclusions:

  • The demonstrated method enables efficient information processing in artificial chemistries.
  • Evolved metabolisms can effectively control robot navigation.
  • The findings show parallels with biological systems like motile bacteria.