%0 Conference Proceedings
%T STARdom: An Architecture for Trusted and Secure Human-Centered Manufacturing Systems
%+ Jozef Stefan International Postgraduate School [Ljubljana, Slovenia]
%+ Jozef Stefan Institute [Ljubljana] (IJS)
%+ QLECTOR d.o.o.
%+ University of West Attica [Athens] (UNIWA)
%+ Ubitech
%+ R2M Solution
%+ University of Cagliari
%+ Department of Mathematics and Computer Science / Dipartimento di Scienze Matematiche e Informatiche "Roberto Magari" (DSMI)
%+ Department of Digital Systems [Athens]
%+ Department of Electrical and Computer Engineering
%+ Intrasoft International
%A Rožanec, Jože, M.
%A Zajec, Patrik
%A Kenda, Klemen
%A Novalija, Inna
%A Fortuna, Blaž
%A Mladenić, Dunja
%A Veliou, Entso
%A Papamartzivanos, Dimitrios
%A Giannetsos, Thanassis
%A Menesidou, Sofia, Anna
%A Alonso, Rubén
%A Cauli, Nino
%A Recupero, Diego, Reforgiato
%A Kyriazis, Dimosthenis
%A Sofianidis, Georgios
%A Theodoropoulos, Spyros
%A Soldatos, John
%Z Part 5: Digital Twins Based on Systems Engineering and Semantic Modeling
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B IFIP International Conference on Advances in Production Management Systems (APMS)
%C Nantes, France
%Y Alexandre Dolgui
%Y Alain Bernard
%Y David Lemoine
%Y Gregor von Cieminski
%Y David Romero
%I Springer International Publishing
%3 Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
%V AICT-633
%N Part IV
%P 199-207
%8 2021-09-05
%D 2021
%R 10.1007/978-3-030-85910-7_21
%K Industry 4.0
%K Smart manufacturing
%K Explainable Artificial Intelligence (XAI)
%K Active learning
%K Demand forecasting
%Z Computer Science [cs]Conference papers
%X There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users’ feedback and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed at all levels. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study.
%G English
%Z TC 5
%Z WG 5.7
%2 https://inria.hal.science/hal-03806488/document
%2 https://inria.hal.science/hal-03806488/file/520761_1_En_21_Chapter.pdf
%L hal-03806488
%U https://inria.hal.science/hal-03806488
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-APMS
%~ IFIP-WG5-7
%~ IFIP-AICT-633