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Controlled self-organisation using learning classifier systems

By: Contributor(s): Material type: ArticlePublication details: KIT Scientific Publishing 2009Description: 1 electronic resource (XXV, 218 p. p.)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783866444317
Subject(s): Online resources: Summary: The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed.
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The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed.

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eng

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