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  • Controlled self-organisation using learning classifier systems

    Urban Maximilian Richter

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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.

    Umfang: XXV, 218 S.

    Preis: €33.50 | £31.00 | $59.00

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    Empfohlene Zitierweise
    Richter, U. 2009. Controlled self-organisation using learning classifier systems. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000013138
    Richter, U.M., 2009. Controlled self-organisation using learning classifier systems. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000013138
    Richter, U M. Controlled Self-organisation Using Learning Classifier Systems. KIT Scientific Publishing, 2009. DOI: https://doi.org/10.5445/KSP/1000013138
    Richter, U. M. (2009). Controlled self-organisation using learning classifier systems. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000013138
    Richter, Urban Maximilian. 2009. Controlled Self-organisation Using Learning Classifier Systems. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000013138




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    Weitere Informationen

    Veröffentlicht am 30. November 2009

    Sprache

    Englisch

    Seitenanzahl:

    244

    ISBN
    Paperback 978-3-86644-431-7

    DOI
    https://doi.org/10.5445/KSP/1000013138