Communications of the ACM
The Vision of Autonomic Computing
Computer
Ant Colony Optimization
Organic Computing - A New Vision for Distributed Embedded Systems
ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Organic Computing - Addressing Complexity by Controlled Self-Organization
ISOLA '06 Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation
Emergence in organic computing systems: discussion of a controversial concept
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
Managing trust in distributed agent systems
ATC'06 Proceedings of the Third international conference on Autonomic and Trusted Computing
Adaptive control of sensor networks
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
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The increasing presence of application scenarios which are based on large collections of active components having to adapt continuously to changing environmental requirements has led to several research initiatives with the objective to create new concepts for the design and operation of environment-mediated multiagent systems. In particular, Autonomic Computing (AC) and Organic Computing (OC) have developed the vision of systems possessing life-like properties: They self-organize, adapt to their dynamically changing environments, and establish other so-called self-x properties, like self-healing, self-configuration, self-optimization etc. The impact of these initiatives will depend crucially on our ability to demonstrate the benefits of these systems with respect to some essential properties. Therefore, we need a clear understanding of some key notions like adaptivity, robustness, flexibility, or their degree of autonomy, allowing for self-x properties.In this paper, a system classification of robust, adaptable, and adaptive systems is presented. Furthermore, a degree of autonomy is characterized to be able to quantify how autonomously a system is working. The degree of autonomy distinguishes and measures external control which is exhibited 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). Finally, learning and of trustworthiness are briefly addressed, since these are further essential aspects of self-organizing, adaptive systems.