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Authors: Bruno Dato ; Marie-Pierre Gleizes and Frédéric Migeon

Affiliation: Université Toulouse III Paul Sabatier, Toulouse, France

Keyword(s): Multi-Agent System Learning, Local Active Learning, Context Learning.

Abstract: In this paper, we place ourselves in the context learning approach and we aim to show that adaptive multi-agent systems are a relevant solution to its enhancement with local active learning strategy. We use a local learning approach inspired by constructivism: context learning by adaptive multi-agent systems. We seek to introduce active learning requests as a mean of internally improving the learning process by detecting and resolving imprecisions between the learnt knowledge. We propose a strategy of local active learning for resolving learning inaccuracies. In this article, we evaluate the performance of local active learning. We show that the addition of active learning requests facilitated by self-observation accelerates and generalizes learning, intelligently selects learning data, and increases performance on prediction errors.

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Paper citation in several formats:
Dato, B.; Gleizes, M. and Migeon, F. (2021). A Local Active Learning Strategy by Cooperative Multi-Agent Systems. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 406-413. DOI: 10.5220/0010328704060413

@conference{icaart21,
author={Bruno Dato. and Marie{-}Pierre Gleizes. and Frédéric Migeon.},
title={A Local Active Learning Strategy by Cooperative Multi-Agent Systems},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2021},
pages={406-413},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010328704060413},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - A Local Active Learning Strategy by Cooperative Multi-Agent Systems
SN - 978-989-758-484-8
IS - 2184-433X
AU - Dato, B.
AU - Gleizes, M.
AU - Migeon, F.
PY - 2021
SP - 406
EP - 413
DO - 10.5220/0010328704060413
PB - SciTePress

- Science and Technology Publications, Lda.
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