Artificial Immunology for Collective Adaptive Systems Design and Implementation

Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their control mechanisms to deal with an unpredictable and changing environment. Existing frameworks for engineering self-adaptive systems fail to account for the need to incorporate self-expression—that is, the capability of a system to dynamically adapt its coordination pattern during runtime. Although the benefits of incorporating self-expression are well known, currently there is no principled means of enabling this during system design. We propose a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system. The framework is described as a set of design principles and customizable algorithms and then is instantiated in three case studies, including two from robotics and one from artificial chemistry. We show that it enables self-expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during runtime to optimize functional and nonfunctional goals, as well as to discover novel patterns and architectures

Artificial immune systems

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm