Agent-based modeling and simulation is a way to model the behavior of populations of components and their interactions within a system. The key of this approach is to model the components of the system as autonomous agents and to simulate their behavior for evaluating the system as a whole. That is very useful for observing the emergence of properties in social, biological, environmental or financial systems, among others.Artificial immune systems, which is a subfield of artificial intelligence, comprises systems which models refer to simplifications of biological immune system models. If agentbased modeling and simulation is used as a laboratory for understanding the biological immune system then, it can also be used for transferring the observed principles into artificial immune system models or for evaluating models that have been already adapted for solving technical problems. This paper presents first a methodology for transferring principles of the biological immune system into the field of artificial immune systems. Then, it presents a brief explanation of the behavior of the cells of the biological immune system, which are treated as internal agents inside a biological organism. Afterwards, the modeling of some selected types of cells and the simulation of the whole simplified system are presented. In the end, the principles of that simplified system are transferred into the design of an alarm management system for the smart grid.Keywords-artificial immune systems; agent-based modeling; agent-based simulation; agent-based modeling and simulation; intelligent agents; emergence; security; smart grid 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops 978-0-7695-4669-8/12 $26.00
FPGAs can be used for the design of autonomic reliable systems. Advantages are reconfiguration and flexibility in the design. However commercial FPGAs are first prone to errors. Second, the design flow is not yet supported for the use of fault tolerance techniques like Built-In Self-Tests. Fault tolerance can be reached through error detection and fault recovery. Most error detection techniques are not suitable for on-line detection because of detection times and long and inflexible training. This paper proposes a fault tolerant design for FPGAs. It has a Built-In Self-Test which error evaluation and fault recovery is supported by computing techniques inspired in the Immune System. A fault recovery and a hardware implementation model are also to be presented.
Abstract. Self-optimizing mechatronic systems have the ability to adjust their goals and behavior according to changes of the environment or system by means of complex real-time coordination and reconfiguration in the underlying software and hardware. In this paper we sketch a generic software architecture for mechatronic systems with selfoptimization and outline which analogies between this architecture and the information processing in natural organisms exist. The architecture at first exploits the ability of its subsystems to adapt their resource requirements to optimize its performance with respect to the usage of available computational resources. Secondly, the architecture achieves, inspired by the acute stress response of a natural being, that in the case of an emergency it makes all recources available to address a given threat in a self-coordinated manner.
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