Designing Self-Aware Adaptive Systems: from Autonomic Computing to Cognitive Immune Networks
Cabri, G. (2013). Designing Self-Aware Adaptive Systems: from Autonomic Computing to Cognitive Immune Networks. In: (Ed.) Proceedings of SASO Workshops 2013, , () ( ed.). (pp. ). : . IEEE.
An autonomic system is composed of ensembles of heterogeneous autonomic components in which large sets of
components are dynamically added and removed. Nodes within
such an ensemble should cooperate to achieve system or human goals, and systems are expected to self-adapt with little or no human-interaction. Designing such systems poses significant challenges. In this paper we propose that the system engineer might gain significant inspiration by looking to the biological immune system, particularly by adopting a perspective on the immune system proposed by Cohen known as the Cognitive Immune Network. The goal of this paper is to show how the current literature in autonomic computing could be positively enriched by considering alternative design processes based on cognitive immune networks. After sketching out the mapping in commonalities between the Cognitive Immune Network and the autonomic computing reference model, we demonstrate how these considerations regarding the design process can be exploited with
an engineered autonomic system by describing experiments with a simple robotic swarm scenario.
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The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.