Self-adaptive Complex Systems

Gleizes, Marie-Pierre

doi:10.1007/978-3-642-34799-3_8

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…A robotic device is responsible of a particular functionality (as described in Section 2.2). ALEX has been built upon the Adaptive Multi-Agent System approach (AMAS) [25], which addresses the problems of complex systems with a bottom-up approach where the concept of cooperation acts as the core of self-organization. The theorem of functional adequacy [26] states: ''for all functionally adequate systems, there is at least one system with an internal cooperative state that realizes the same function in the same environment''.…”

Section: Alex: Adaptive Learner By Experimentsmentioning

confidence: 99%

Principles and experimentations of self-organizing embedded agents allowing learning from demonstration in ambient robotics

Verstaevel

Régis

Gleizes

et al. 2016

Future Generation Computer Systems

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Ambient systems are populated by many heterogeneous devices to provide adequate services to their users. The adaptation of an ambient system to the specific needs of its users is a challenging task. Because human-system interaction has to be as natural as possible, we propose an approach based on Learning from Demonstration (LfD). LfD is an interesting approach to generalize what has been observed during the demonstration to similar situations. However, using LfD in ambient systems needs adaptivity of the learning technique. We present ALEX, a multi-agent system able to dynamically learn and reuse contexts from demonstrations performed by a tutor. The results of the experiments performed on both a real and a virtual robot show interesting properties of our technology for ambient applications.

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Section: Alex: Adaptive Learner By Experimentsmentioning

confidence: 99%

Principles and experimentations of self-organizing embedded agents allowing learning from demonstration in ambient robotics

Verstaevel

Régis

Gleizes

et al. 2016

Future Generation Computer Systems

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“…In the Adaptive Multi-Agent System Theory [11], agents are defined as autonomous, adaptive and cooperative entities that possess local objective. The local cooperation between agents allows the system to self-adapt in order to realize the function for which it is designed.…”

Section: Atlasmentioning

confidence: 99%

“…Self-adaptive multi-agent systems as defined in [11] with their ability to take into account a large number of entities and constraints, naturally provide self-adaptation and selforganization capabilities required to solve this kind of problem. To build our system, we rely on AMAS4Opt (Adaptive MultiAgent System For Optimization) [14], a generic agent model which provides design patterns to solve optimization problems using cooperative self-adaptive multi-agent systems as defined in the Adaptive Multi-Agent System (AMAS) Theory [11].…”

Section: Introductionmentioning

confidence: 99%

“…To build our system, we rely on AMAS4Opt (Adaptive MultiAgent System For Optimization) [14], a generic agent model which provides design patterns to solve optimization problems using cooperative self-adaptive multi-agent systems as defined in the Adaptive Multi-Agent System (AMAS) Theory [11]. In this model, agents are designed as close as possible to the natural description if the entities of the problem.…”

Section: Introductionmentioning

confidence: 99%

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Multi-satellite Mission Planning Using a Self-Adaptive Multi-agent System

Bonnet¹,

Gleizes

Kaddoum

et al. 2015

2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18173 Abstract-Mission planning for a constellation of Earth observation satellites is a complex problem raising significant technological challenges for tomorrow's space systems. The large numbers of customers requests and their dynamic introduction in the planning system result in a huge combinatorial search space with a potentially highly dynamical evolution requirements. The techniques used nowadays have several limitations, in particular, it is impossible to dynamically adapt the plan during its construction even for small modifications. Satellites of a constellation are planned in a chronological way instead of a more collective planning which can provide additional load balancing.In this paper, we propose to solve this difficult and highly dynamic problem using adaptive multi-agent systems, taking advantage from their self-adaptation and self-organization mechanisms. In the proposed system, the agents, through their local interactions, allow to dynamically reach a good solution, while ensuring a controlled distribution of tasks within the constellation of satellites. Finally, a comparison with a classical chronological greedy algorithm, commonly used in the spatial domain, highlights the advantages of the presented system.

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“…Thus, the focus is on the local behaviour of the agents. The global function of the system emerges based on some heuristics independent from the overall function like cooperation for example (the case of the AMAS theory [2]). …”

Section: Introductionmentioning

confidence: 99%