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Lifelong Machine Learning with Adaptive Multi-Agent Systems

Abstract : 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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https://hal.archives-ouvertes.fr/hal-01712545
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Submitted on : Monday, February 19, 2018 - 4:02:14 PM
Last modification on : Tuesday, September 8, 2020 - 10:34:04 AM
Long-term archiving on: : Monday, May 7, 2018 - 9:13:30 PM

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  • HAL Id : hal-01712545, version 1
  • OATAO : 18858

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Nicolas Verstaevel, Jérémy Boes, Julien Nigon, Dorian d'Amico, Marie-Pierre Gleizes. Lifelong Machine Learning with Adaptive Multi-Agent Systems. 9th International Conference on Agents and Artificial Intelligence (ICAART 2017), Feb 2017, Porto, Portugal. pp. 275-286. ⟨hal-01712545⟩

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