Lifelong Machine Learning with Adaptive Multi-Agent Systems

Nicolas Verstaevel, Jérémy Boes, Julien Nigon, Dorian d'Amico, Marie-Pierre Gleizes


Sensors and actuators are progressively invading our everyday life as well as industrial processes. They form complex and pervasive systems usually called ”ambient systems” or ”cyber-physical systems”. These systems are supposed to efficiently perform various and dynamic tasks in an ever-changing environment. They need to be able to learn and to self-adapt throughout their life, because designers cannot specify a priori all the interactions and situations they will face. These are strong requirements that push the need for lifelong machine learning, where devices can learn models and behaviours during their whole lifetime and are able to transfer them to perform other tasks. This paper presents a multi-agent approach for lifelong machine learning.


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Paper Citation

in Harvard Style

Verstaevel N., Boes J., Nigon J., d'Amico D. and Gleizes M. (2017). Lifelong Machine Learning with Adaptive Multi-Agent Systems . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 275-286. DOI: 10.5220/0006247302750286

in Bibtex Style

author={Nicolas Verstaevel and Jérémy Boes and Julien Nigon and Dorian d'Amico and Marie-Pierre Gleizes},
title={Lifelong Machine Learning with Adaptive Multi-Agent Systems},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Lifelong Machine Learning with Adaptive Multi-Agent Systems
SN - 978-989-758-220-2
AU - Verstaevel N.
AU - Boes J.
AU - Nigon J.
AU - d'Amico D.
AU - Gleizes M.
PY - 2017
SP - 275
EP - 286
DO - 10.5220/0006247302750286