Swarm Intelligence in Autonomic Computing

2011 ◽  
pp. 97-117 ◽  
Author(s):  
Christos Anagnostopoulos ◽  
Stathes Hadjiefthymiades
Keyword(s):  
Swarm Intelligence ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Dynamic Environments ◽  
Self Organization ◽  
Context Aware ◽  
Multi Agent

Autonomic computing has become increasingly popular during recent years. Many mobile autonomic and context-aware applications exhibit self-organization in dynamic environments adopted from multi-agent, or swarm, research. The basic paradigm behind swarm systems is that tasks can be more efficiently dispatched through the use of multiple, simple autonomous agents instead of a single, sophisticated one. Such systems are much more adaptive, scalable, and robust than those based on a single, highly capable, agent. A swarm system can generally be defined as a decentralized group (swarm) of autonomous agents (particles) that are simple, with limited processing capabilities. Particles must cooperate intelligently to achieve common tasks.

scholarly journals A Programming Approach to Collective Autonomy

2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Roberto Casadei ◽  
Gianluca Aguzzi ◽  
Mirko Viroli
Keyword(s):  
Autonomous Vehicles ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Programming Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulated Case ◽  
Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

scholarly journals AGENT-BASED, CONTEXT-AWARE INFORMATION SHARING FOR AMBIENT INTELLIGENCE

2011 ◽  
Vol 20 (06) ◽  
pp. 985-1000 ◽  
Author(s):  
ANDREI OLARU ◽  
CRISTIAN GRATIE
Keyword(s):  
Ambient Intelligence ◽  
Self Organization ◽  
Context Aware ◽  
Multi Agent System ◽  
Interconnected Network ◽  
Agent Based ◽  
Surrounding Environment ◽  
Multi Agent ◽  
The One ◽  
Future Vision

In a future vision of Ambient Intelligence — or AmI — our surrounding environment will integrate a pervasive, interconnected network of sensors, intelligent appliances and computer-like devices. This implies, on the one hand, hardware and interface related issues, and, on the other hand, a layer of context-aware services that manages the large quantities of information generated throughout a system formed mostly of devices with limited capabilities. This paper presents the first steps toward the realization of the AmIciTy framework: a multi-agent system that relies on local interaction and the self-organization of agents, having as purpose the context-aware sharing of pieces of information. The paper presents the structure of the system, the design of the agents, the manner of building scenarios, experiments and the evaluation of a prototype.

EDITORIAL

2012 ◽  
Vol 21 (03) ◽  
pp. 1202002
Author(s):  
ZHIHUA CUI ◽  
ZHONGZHI SHI ◽  
RAJAN ALEX
Keyword(s):  
Swarm Intelligence ◽  
Decentralized Control ◽  
Intelligent Systems ◽  
Wide Spectrum ◽  
Self Organization ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Agent Systems ◽  
The World ◽  
Multi Agent

Swarm intelligence is an umbrella for amount optimization algorithms. This discipline deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In general, multi-agent systems that use some swarm intelligence are said to be swarm intelligent systems. They are mostly used as search engines and optimization tools. The goal of this special issue has been to offer a wide spectrum of sample works throughout the world about innovative methodologies of swarm intelligence. The issue should be useful both for beginners and experienced researchers in the field of computational intelligence.

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