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Adaptivity and Self-Organisation in Organic Computing Systems


Hartmut Schmeck, Christian Müller-Schloer, Emre Cakar, Moez Mnif, Urban Richter



Veröffentlicht: 2010 September
Erscheinungsort: New York, NY, USA
Journal: ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Nummer: 3
Seiten: 10:1-10:32
Verlag: ACM
Volume: 5

Referierte Veröffentlichung
BibTeX




Kurzfassung
Organic Computing (OC) and other research initiatives like Autonomic Computing or Proactive Computing have developed the vision of systems possessing life-like properties: They self-organise, adapt to their dynamically changing environments, and establish other so-called self-x-properties, like self-healing, self-configuration, self-optimisation, etc. What we are searching for in OC are methodologies and concepts for systems that allow to cope with increasingly complex networked application systems by introduction of self-x-properties and at the same time guarantee a trustworthy and adaptive response to externally provided system objectives and control actions. Therefore, in OC, we talk about controlled self-organisation.

Although the terms self-organisation and adaptivity have been discussed for years, we miss a clear definition of self-organisation in most publications, which have a technically motivated background.

In this article, we briefly summarise the state of the art and suggest a characterisation of (controlled) self-organisation and adaptivity that is motivated by the main objectives of the OC initiative. We present a system classification of robust, adaptable, and adaptive systems and define a degree of autonomy to be able to quantify how autonomously a system is working. The degree of autonomy distinguishes and measures external control that is exerted directly by the user (no autonomy) from internal control of a system which might be fully controlled by an observer/controller architecture that is part of the system (full autonomy). The quantitative degree of autonomy provides the basis for characterising the notion of controlled self-organisation. Furthermore, we discuss several alternatives for the design of organic systems.

ISSN: 1556-4665
DOI Link: 10.1145/1837909.1837911

Projekt

OCCSQE



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet
Organic Computing