Christine Régis
Paul Sabatier University
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Publication
Featured researches published by Christine Régis.
practical applications of agents and multi-agent systems | 2009
Jean-Pierre Georgé; Sylvain Peyruqueou; Christine Régis; Pierre Glize
Hydrological phenomena are often very dynamic and depend on numerous criteria. The STAFF software is an adaptive model for flood forecast based on self-organizing multiagent systems. It is operational since 2002 in the Midi-Pyrenees region in France. The aim of this paper is to show the relevance of our approach to model complex natural systems by focusing on the results, architecture and self-organization mechanisms of a real world application. The main idea is to let the artificial system self-adapt towards the adequate model by confronting it to real data, thus ensuring that the resulting model represents reality. Moreover, since the MAS is constantly adapting, we obtain a dynamic and autonomous system that can take into account any future dynamics (strong perturbations, sensor breakdowns...) and able to provide decision-makers with usable information anytime.
Future Generation Computer Systems | 2016
Nicolas Verstaevel; Christine Régis; Marie Pierre Gleizes; Fabrice Robert
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. Extreme Sensitive Robotic as a bottom-up approach to deal with complexity in ambient robotic.Self-adaptive multi-agent system for learning from demonstration in ambient robotic.Experiments show that tutorship learning with Context agent is promising.
international conference on agents and artificial intelligence | 2015
Nicolas Verstaevel; Christine Régis; Valerian Guivarch; Marie Pierre Gleizes; Fabrice Robert
Our work focuses on Extreme Sensitive Robotic that is on multi-robot applications that are in strong interaction with humans and their integration in a highly connected world. Because human-robots interactions have to be as natural as possible, we propose an approach where robots Learn from Demonstrations, memorize contexts of learning and self-organize their parts to adapt themselves to new contexts. To deal with Extreme Sensitive Robotic, we propose to use both an Adaptive Multi-Agent System (AMAS) approach and a Context-Learning pattern in order to build a multi-agent system ALEX (Adaptive Learner by Experiments) for contextual learning from demonstrations.
international conference ambient systems networks and technologies | 2015
Nicolas Verstaevel; Christine Régis; Marie Pierre Gleizes; Fabrice Robert
Ambient systems are populated by many heterogeneous devices to provide adequate services to its 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 (Lf D). However, using Lf D 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. Results of experiments performed on both a real and a virtual robot show interesting properties of our technology for ambient applications.
international conference on computational collective intelligence | 2013
Tom Jorquera; Jean-Pierre Georgé; Marie Pierre Gleizes; Christine Régis
While multi-agent systems have been successfully applied to combinatorial optimization, very few works concern their applicability to continuous optimization problems. In this article we propose a framework for modeling a continuous optimization problems as multi-agent system, which we call NDMO, by representing the problem as an agent graph, and complemented with optimization solving behaviors. Some of the results we obtained with our implementation on several continuous optimization problems are presented.
self-adaptive and self-organizing systems | 2012
Tom Jorquera; Jean-Pierre Georgé; Christine Régis
Multidisciplinary optimization (MDO) problems are a specific class of optimization problem where the number of variables and disciplines involved is to important to directly apply classical optimization methods. Most of the existing approaches concentrate on separating the problem in distinct sub problems and using standard optimization methods on these sub problems while trying to maintain consistency among the variables shared by the sub problems. Basically these methods try to help the user to find an optimization process which reduces the complexity of the problem. However, a shortcoming of these MDO methods is that they require a strong expert knowledge of both the problem to be solved and the method which is applied, in order to obtain interesting results.
8th European Congress on Embedded Real Time Software and Systems (ERTS 2016) | 2016
Nicolas Verstaevel; Christine Régis; Marie Pierre Gleizes; Fabrice Robert
Archive | 2015
Nicolas Verstaevel; Christine Régis; Marie Pierre Gleizes; Fabrice Robert
JFSMA | 2015
Nicolas Verstaevel; Christine Régis; Marie Pierre Gleizes; Fabrice Robert
international conference on agents and artificial intelligence | 2013
Tom Jorquera; Jean-Pierre Georgé; Marie Pierre Gleizes; Christine Régis; Pierre Glize