01651nam 2200361z- 450 9910346920203321202102111-000-01313-8(CKB)4920000000101320(oapen)https://directory.doabooks.org/handle/20.500.12854/44037(oapen)doab44037(EXLCZ)99492000000010132020202102d2009 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierControlled self-organisation using learning classifier systemsKIT Scientific Publishing20091 online resource (XXV, 218 p. p.)3-86644-431-1 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.controlled self-organisationextended learning classifier systemmulti-agent simulationobserver/controller architectureorganic computingRichter Urban Maximilianauth1325328BOOK9910346920203321Controlled self-organisation using learning classifier systems3036750UNINA