Prediction Error-Driven Memory Consolidation for Continual Learning: On the Case of Adaptive Greenhouse Models

  • This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of an episodic memory system and of prediction-error driven memory consolidation. In line with evidence from brain sciences, memories are retained depending on their congruence with the prior knowledge stored in the system. In this work, congruence is estimated in terms of prediction error resulting from a deep neural model. The proposed AI system is transferred onto an innovative application in the horticulture industry: the learning and transfer of greenhouse models. This work presents models trained on data recorded from research facilities and transferred to a production greenhouse.

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Metadaten
Author:Guido SchillaciORCiD, Uwe Schmidt, Luis MirandaORCiD
URN:urn:nbn:de:kobv:eb1-opus-2510
DOI:https://doi.org/10.1007/s13218-020-00700-8
ISSN:1610-1987
Parent Title (English):KI - Künstliche Intelligenz
Publisher:Springer
Document Type:Article
Language:English
Year of Completion:2021
Date of Publication (online):2022/06/17
Date of first Publication:2021/01/27
Publishing Institution:Hochschule für nachhaltige Entwicklung Eberswalde
Release Date:2022/06/17
Tag:Adaptive models; Deep neural networks; Episodic memory; Greenhouse; Memory consolidation
Volume:35
Page Number:10
First Page:71
Last Page:80
Institutions / Departments:Fachbereich Wald und Umwelt
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
open_access (DINI-Set):open_access
University Bibliography:University Bibliography
Zweitveröffentlichung
Peer-Review / Referiert
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
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