Artificial Immunology for Collective Adaptive Systems Design and Implementation

Capodieci, Nicola; Hart, Emma; Cabri, Giacomo

doi:10.1145/2897372

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…The DEECo system applies dynamic architecture abstractions of autonomous components and component ensembles that provide a straightforward reflection of operational goals in the application architecture [45]. Capodieci et al [46] proposed a conceptual framework for the design of systems that is based on inspiration from the natural immune system.…”

Section: A System Architecture From Control Point Of Viewmentioning

confidence: 99%

A Review of the Principles of Designing Smart Cyber-Physical Systems for Run-Time Adaptation: Learned Lessons and Open Issues

Tavčar

Horváth

2019

IEEE Trans. Syst. Man Cybern, Syst.

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Smart cyber-physical systems (S-CPSs) are complex engineered systems empowered by cyber-physical computing and equipped with the capability of reasoning, learning, adapting, and evolving. As an outcome of data-driven dynamic computing, reasoning capabilities, and the run-time obtained own knowledge, nonlinear and emergent behavior of S-CPSs whilst in operation is an open issue, not experienced in the case of conventional technical systems. This paper analyzes the technical issues of run-time operation and emergent behavior of S-CPSs, reviews the current understanding and state of advancement in designing S-CPSs for run-time, explores the paradox, and issues of designing for runtime adaptation, and synthesizes some general principles that can be taken into consideration when addressing the challenges, first of all, in the context of advanced manufacturing systems. This paper introduces four levels of CPSs according to reasoning capabilities and adaptation freedom of systems, and recognizes the paradox that a system with a higher level of freedom requires a higher level of self-control and resource management according to the overall objective of operation. Specific and common design principles are presented and critically assessed for each advancement level of CPSs. The principles synthesized by the authors provide only a partial fulfillment of the generic need. The planned future research addresses these issues and proposes (largely implementation and application independent) genuine principles for system developers.

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Section: A System Architecture From Control Point Of Viewmentioning

confidence: 99%

A Review of the Principles of Designing Smart Cyber-Physical Systems for Run-Time Adaptation: Learned Lessons and Open Issues

Tavčar

Horváth

2019

IEEE Trans. Syst. Man Cybern, Syst.

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“…For example, evolutionary algorithms are used in [30] to adjust the parameters of a robot's ruleset during the course of a 'lifetime' to cope with a dynamically changing environment. [31] use a distributed algorithm based on an artificial-immune system algorithm (AIS) within a swarm robotics application to enable robots to adapt their individual foraging strategies over time based on available resources, environmental conditions and behaviours of other robots to maximise foraging.…”

Section: Bio-inspired Computationmentioning

confidence: 99%

For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems

Pitt¹,

Hart²

2017

2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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This position paper is concerned with the challenge of engineering multi-scale and long-lasting systems, whose operation is regulated by sets of mutually-agreed, conventional rules. The core of the problem is that there are multiple, inter-dependent dimensions of flux, with numerous contextual factors to take into account. These dimensions of flux include, on the one hand, the set of rules itself; and on the other, the system components (population), their social network, and the operating environment. However, there appears to be no 'one size fits all' optimum ruleset for all combinations of population, social network and environment; nor (given the contextual factors) is there a planning-type algorithm that can compute an 'ideal' ruleset for any particular combination of population, social network and environment. These features of the problem suggest that recent advances in machine learning and evolutionary computation can provide the instruments for facilitating self-adaptation of a rule-based system over different timescales. This paper proposes that the integration of concepts from socially-and biologically-inspired computing can pave the way for eventual development of a computational framework that will enable principled (methodological) development of sustainable adaptive rule-based systems.

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“…Also, in [14], a framework based on component-based viewpoint is introduced. A conceptual framework for designing autonomic components starting from the artificial immune system paradigm have been proposed in [11]. It covers the self-expression aspect of system based on inspiration from the natural immune system.…”

Section: Background and Related Workmentioning

confidence: 99%

Enabling Autonomic Computing Support for the JADE Agent Platform

Farahani¹,

Nazemi²,

Cabri³

et al. 2017

SCPE

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Engineering complex distributed system is a real challenge documented in recent literature. Novel paradigms such as Autonomic Computing (AC) approaches appear to be the fittest engineering model in order to face performance instabilities of systems inserted in open and non-deterministic environments. To this purpose, the availability of appropriate development environments will facilitate the design of such systems. Standard agent development platforms represent a good starting point, but they generally lack of rigorous ways to define central AC-related concepts such as the prominent role of feedback loops and knowledge integration in decision making processes: we therefore believe that Agent Oriented Software Engineering (AOSE) can be substantially enriched by taking into account such concepts.In this paper, a novel extension of the well-known JADE agent development environment is discussed. This extension enhances JADE in order to address the engineering process with Autonomic Computing support. It is called "Autonomic Computing Enabled JADE" or shortly ACE-JADE. The behavioral model of ACE-JADE will be thoroughly described in the context of a case study (NASA ANTS project).

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Artificial Immunology for Collective Adaptive Systems Design and Implementation

Cited by 7 publications

References 34 publications

A Review of the Principles of Designing Smart Cyber-Physical Systems for Run-Time Adaptation: Learned Lessons and Open Issues

A Review of the Principles of Designing Smart Cyber-Physical Systems for Run-Time Adaptation: Learned Lessons and Open Issues

For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems

Enabling Autonomic Computing Support for the JADE Agent Platform

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