Adelfe 2.0

Bonjean, Noélie; Mefteh, Wafa; Gleizes, Marie Pierre; Maurel, Christine; Migeon, Frédéric

doi:10.1007/978-3-642-39975-6_3

Cited by 23 publications

(17 citation statements)

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“…In the work of Gershenson, an interesting clue is given when self-organisation is defined: it says that "the elements need to divide, but also to integrate, the problem". Similarly, the ADELFE method [10], which supports the applying of the AMAS approach, suggests that the problem domain should help the decomposition in agents. But for all these works, no explanation nor rationalisation are given to support these recommendations.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Methodological Guidelines for Engineering Self-organization and Emergence

Noël

Zambonelli

2015

Lecture Notes in Computer Science

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The ASCENS project deals with the design and development of complex self-adaptive systems, where self-organization is one of the possible means by which to achieve self-adaptation. However, to support the development of self-organising systems, one has to extensively re-situate their engineering from a software architectures and requirements point of view. In particular, in this chapter, we highlight the importance of the decomposition in components to go from the problem to the engineered solution. This leads us to explain and rationalise the following architectural strategy: designing by following the problem organisation. We discuss architectural advantages for development and documentation, and its coherence with existing methodological approaches to self-organisation, and we illustrate the approach with an example on the area of swarm robotics

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Section: Related Work and Discussionmentioning

confidence: 99%

Methodological Guidelines for Engineering Self-organization and Emergence

Noël

Zambonelli

2015

Lecture Notes in Computer Science

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“…In the current state of the approach, the AMAS designer has to design the cooperative behavior for each NCS. A methodology named ADELFE (French acronym for Toolkit for Developing Software with Emergent Functionalities) guides the design of AMASs ( Bonjean et al, 2014 ). It is a bottom-up and iterative design process that encourages the designer to focus on the local function of each agent, and to forget the global function of the system.…”

Section: Adaptive Multi-agent Systemsmentioning

confidence: 99%

Self-organizing multi-agent systems for the control of complex systems

Boes

Migeon

2017

Journal of Systems and Software

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a b s t r a c tBecause of the law of requisite variety, designing a controller for complex systems implies designing a complex system. In software engineering, usual top-down approaches become inadequate to design such systems. The Adaptive Multi-Agent Systems (AMAS) approach relies on the cooperative self-organization of autonomous micro-level agents to tackle macro-level complexity. This bottom-up approach provides adaptive, scalable, and robust systems. This paper presents a complex system controller that has been designed following this approach, and shows results obtained with the automatic tuning of a real internal combustion engine.

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“…An interaction between two agents is said to be cooperative if this interaction does not impair the two agents to perform their tasks. When in a cooperative situation, an agent executes its nominal behaviour to perform its tasks [4]. So, for the whole AMOEBA system to work properly, we must ensure that each agent is in a cooperative situation as often as possible, so it performs its nominal behaviour correctly.…”

Section: Amoeba: a Generic Ai System For Smart Citiesmentioning

confidence: 99%

Use Cases of Pervasive Artificial Intelligence for Smart Cities Challenges

Nigon

Glize²,

Dupas

et al. 2016

2016 Intl IEEE Conferences on Ubiquitous Intelligence &Amp; Computing, Advanced and Trusted Computing, Scalable Computing and C

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Software engineering has been historically topdown. From a fully specified problem, a software engineer needs to detail each step of the resolution to get a solution. The resulting program will be functionally adequate as long as its execution environment complies with the original specifications. With their large amount of data and their ever changing multi-level dynamics, smart cities are too complex for a topdown approach. They prompt the need for a paradigm shift in computer science. Programs should be able to self-adapt on the fly, to handle unspecified events, and to efficiently deal with tremendous amount of data. To this end, bottom-up approach should become the norm. Machine learning is a first step, and distributed computing helps. Multi-Agent Systems (MAS) can combine machine learning and distributed computing and may be easily designed with a bottom-up approach. This paper explores how MASs can answer challenges at various levels of smart cities, from sensors networks to ambient intelligence.

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Adelfe 2.0

Cited by 23 publications

References 0 publications

Methodological Guidelines for Engineering Self-organization and Emergence

Methodological Guidelines for Engineering Self-organization and Emergence

Self-organizing multi-agent systems for the control of complex systems

Use Cases of Pervasive Artificial Intelligence for Smart Cities Challenges

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