Phdthesis74: Unterschied zwischen den Versionen
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|Instructor=Prof. Dr. H. Schmeck / Prof. Dr. K.-H. Waldmann | |Instructor=Prof. Dr. H. Schmeck / Prof. Dr. K.-H. Waldmann | ||
|Date=2009/07/30 | |Date=2009/07/30 | ||
| + | |School=PhD thesis at the Universität Karlsruhe (TH), Fakultät für Wirtschaftswissenschaften | ||
|Month=Juli | |Month=Juli | ||
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{{Publikation Details | {{Publikation Details | ||
| + | |Abstract=The complexity of technical systems continuously increases, breakdowns and fatal errors occur quite often. Therefore, the mission of organic computing is to tame these challenges by providing appropriate degrees of freedom for self-organised behaviour. Technical systems should adapt to changing requirements of their execution environment, in particular with respect to human needs. To achieve these ambitious goals, adequate methods and techniques have to be developed. The proposed generic observer/controller architecture constitutes one way to achieve controlled self-organisation in technical systems. | ||
| + | To improve the design of organic computing systems, the observer/controller architecture is applied to multi-agent scenarios from the predator/prey domain, which serve as testbeds for evaluation. Thereby, the aspect of on-line learning using learning classifier systems is specially addressed. | ||
| + | |ISBN=978-3-86644-431-7 | ||
| + | |Link=http://digbib.ubka.uni-karlsruhe.de/volltexte/1000013138 | ||
|Projekt=OCCS, QE | |Projekt=OCCS, QE | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
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{{Forschungsgebiet Auswahl | {{Forschungsgebiet Auswahl | ||
|Forschungsgebiet=Agenten Systeme | |Forschungsgebiet=Agenten Systeme | ||
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{{Forschungsgebiet Auswahl | {{Forschungsgebiet Auswahl | ||
|Forschungsgebiet=Maschinelles Lernen | |Forschungsgebiet=Maschinelles Lernen | ||
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{{Forschungsgebiet Auswahl | {{Forschungsgebiet Auswahl | ||
|Forschungsgebiet=Organic Computing | |Forschungsgebiet=Organic Computing | ||
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Version vom 1. Dezember 2009, 10:22 Uhr
Datum: 30. Juli 2009
PhD thesis at the Universität Karlsruhe (TH), Fakultät für Wirtschaftswissenschaften
Referent(en): Prof. Dr. H. Schmeck / Prof. Dr. K.-H. Waldmann
BibTeX
Kurzfassung
The complexity of technical systems continuously increases, breakdowns and fatal errors occur quite often. Therefore, the mission of organic computing is to tame these challenges by providing appropriate degrees of freedom for self-organised behaviour. Technical systems should adapt to changing requirements of their execution environment, in particular with respect to human needs. To achieve these ambitious goals, adequate methods and techniques have to be developed. The proposed generic observer/controller architecture constitutes one way to achieve controlled self-organisation in technical systems.
To improve the design of organic computing systems, the observer/controller architecture is applied to multi-agent scenarios from the predator/prey domain, which serve as testbeds for evaluation. Thereby, the aspect of on-line learning using learning classifier systems is specially addressed.
ISBN: 978-3-86644-431-7
Weitere Informationen unter: Link
Organic Computing, Maschinelles Lernen, Agentensysteme