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 incompetencies leads to active endogenous learning.This work on an adaptive decentralized learning mechanism will be applied on application domains such as robotics, autonomous vehicles and smart cities.
Figure 1: « Schema of the Learning
Figure 2: « Implementation Example Multi-Agent System » on an Industrial Robot »
Scientific Goals
- Design a Self-Learning System
- Lifelong and Endogenous Learning
- Genericity and Scalability
Keywords
Self-Adaptive Learning, Endogenous Learning, Adaptive Multi-Agent Systems, Artificial Intelligence
Contacts
bruno.dato@irit.frfrederic.migeon@irit.fr marie-pierre.gleizes@irit.fr
