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The Self-Adaptive Context Learning Pattern: Overview And Proposal, Jeremy Boes, Julien Nigon, Nicolas R. Verstaevel, Marie-Pierre Gleizes, Frederic Migeon
The Self-Adaptive Context Learning Pattern: Overview And Proposal, Jeremy Boes, Julien Nigon, Nicolas R. Verstaevel, Marie-Pierre Gleizes, Frederic Migeon
SMART Infrastructure Facility - Papers
Over the years, our research group has designed and developed many self-adaptive multi-agent systems to tackle real-world complex problems, such as robot control and heat engine optimization. A recurrent key feature of these systems is the ability to learn how to handle the context they are plunged in, in other words to map the current state of their perceptions to actions and effects. This paper presents the pattern enabling the dynamic and interactive learning of the mapping between context and actions by our multi-agent systems.
Principles And Experimentations Of Self-Organizing Embedded Agents Allowing Learning From Demonstration In Ambient Robotic, Nicolas R. Verstaevel, Christine Regis, Marie-Pierre Gleizes, Fabrice Robert
Principles And Experimentations Of Self-Organizing Embedded Agents Allowing Learning From Demonstration In Ambient Robotic, Nicolas R. Verstaevel, Christine Regis, Marie-Pierre Gleizes, Fabrice Robert
SMART Infrastructure Facility - Papers
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 (LfD). 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. Results of experiments performed on both a real and a virtual robot show interesting properties of our …