Designing Self-Aware Adaptive Systems: From Autonomic Computing to Cognitive Immune Networks

Capodieci, Nicola; Hart, Emma; Cabri, Giacomo

doi:10.1109/sasow.2013.17

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…This work was more recently extended by Whitbrook et al (2010), in which the authors detailed how similar algorithms can be properly transferred in real robots. In Capodieci et al (2013a), the authors proposed that ideas from autonomic computing could be combined with immune-inspiration to provided distributed control. An idiotypic network algorithm was proposed and applied to selecting movement strategies in swarm foraging task in Capodieci et al (2013b).…”

Section: Related Workmentioning

confidence: 99%

Idiotypic Networks for Evolutionary Controllers in Virtual Creatures

Capodieci¹,

Hart²,

Cabri³

2014

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems

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We propose a novel method for evolving adaptive locomotive strategies for virtual limbless creatures that addresses both functional and non-functional requirements, respectively the ability to avoid obstacles and to minimise spent energy. We describe an approach inspired by artificial immune systems, based on a dual-layer idiotypic network that results in a completely decentralised controller. Starting from a system initialised with five non-adaptive locomotion strategies, we show that an adaptive controller can evolve that both minimises energy requirements and maximises distance covered when compared to the initial strategies.

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Section: Related Workmentioning

confidence: 99%

Idiotypic Networks for Evolutionary Controllers in Virtual Creatures

Capodieci¹,

Hart²,

Cabri³

2014

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems

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“…According to a fitness function that takes into account only local information, the concentration that characterizes each antibody inside each swarm individual changes over time so to provide the fittest immunological response in terms of kinetic parameters to be assigned to the swarm individual. The dynamics of this adaptive process follows a design methodology that we have previously formulated [12], [13] and applied in a robotic swarm foraging scenario [14]. We will show how this immune-inspired approach achieves self-organisation within the swarm, resulting in the creation of new shapes and behaviours, starting from a restricted set of recipes previously found with the hyperinteractive method.…”

Section: Related Workmentioning

confidence: 99%

Artificial Immune System Driven Evolution in Swarm Chemistry

Capodieci

Hart

Cabri

2014

2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-Morphogenetic engineering represents an interestingfield in which models, frameworks and algorithms can be tested in order to study how self-* properties and emergent behaviours can arise in potentially complex and distributed systems. In this field, the morphogenetic model we will refer to is swarm chemistry, since a well known challenge in this dynamical process concerns discovering mechanisms for providing evolution within coalescing systems of particles. These systems consist in sets of moving particles able to self-organise in order to create shapes or geometrical formations that provide robustness towards external perturbations. We present a novel mechanism for providing evolutionary features in swarm chemistry that takes inspiration from artificial immune system literature, more specifically regarding idiotypic networks. Starting from a restricted set of chemical recipes, we show that the system evolves to new states, using an autonomous method of detecting new shapes and behaviours free from any human interaction.

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“…Methods have been proposed to adapt autonomic components using evolutionary computing (EC) techniques as a response to environmental changes, e.g. [5,4]. These components exhibit cognition, namely learning and decisionmaking abilities, leading to collective self-awareness.…”

Section: Introductionmentioning

confidence: 99%

Engineering Sustainable and Adaptive Systems in Dynamic and Unpredictable Environments

Cardoso

Rossetti

Hart

et al. 2018

Lecture Notes in Computer Science

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Electronic institutions are socially-inspired multi-agent systems, typically operating under a set of policies, which are required to determine system operation and to deal with violations and other non-compliant behaviour. They are often faced with a dynamic population of agents, social network, and environment and their policy should suit this context. However, there is usually a large space of possible system policies, but no tractable systematic method to find an appropriate policy given a joint state of the population, social network, and the environment. We have developed a model of an energy system which encompasses several interconnected community energy systems. We propose two methods, an offline and an online procedure, which enable this system model to approximately optimise its performance through adaptation and evolution of its operating policy. The policies evolved by our procedures clearly outperform a baseline policy we have designed by hand. Both procedures return policies which are appropriate for a system, given some performance criterion, without a human designer's intervention. This could lay the foundations for the development of a new methodological paradigm for the engineering of collective adaptive systems.

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Designing Self-Aware Adaptive Systems: From Autonomic Computing to Cognitive Immune Networks

Cited by 9 publications

References 16 publications

Idiotypic Networks for Evolutionary Controllers in Virtual Creatures

Idiotypic Networks for Evolutionary Controllers in Virtual Creatures

Artificial Immune System Driven Evolution in Swarm Chemistry

Engineering Sustainable and Adaptive Systems in Dynamic and Unpredictable Environments

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