Context Presentation

The current digital transformation requires the creation of autonomous applications that can be adapted to complex, dynamic, heterogeneous, and unpredictable environments. These systems must be equipped with proactive learning capabilities.

To this end, Self-Adaptive Multi-Agent principle allows the decentralization and self-observation of the learning process. Each knowledge granule is an autonomous agent that cooperates with its neighbors to improve learning from exogenous and endogenous feedbacks. Detecting and solving concurrences, conflicts and incompetence leads to active endogenous learning.This work on an adaptive decentralized learning mechanism is applied to the learning of a robotic arm inverse kinematics.

Illustration Bruno Dato - Bruno Dato


Self-Adaptive Learning, Endogenous Learning, Adaptive Multi-Agent Systems, Robotics

Scientific goals

•    Design a Self-Learning System

•    Lifelong and Endogenous Learning

•    Genericity and Scalability