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.
['Jérémy Boes', 'Julien Nigon', 'Nicolas Verstaevel', 'Marie Pierre Gleizes', 'Frédéric Migeon']
The Self-Adaptive Context Learning Pattern: Overview and Proposal