Hybrid Human-AI Decision Support for Enhanced Human Empowerment in Dynamic Situations

Informazioni relative al progetto

HumAIne

ID dell’accordo di sovvenzione: 101120218

DOI

10.3030/101120218

Data della firma CE 11 Settembre 2023

Data di avvio 1 Ottobre 2023

Data di completamento 30 Settembre 2026

Finanziato da

• Digital, Industry and Space

Costo totale

€ 7 658 375,00

Contributo UE

€ 7 658 375,00

Coordinato da

GFT ITALIA SRL
Italy

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (14)

Reference Architecture and Specifications v2

Documentation of the HumAIne RA (T2.6) and technical specifications. Including task and workflow modelling (T2.2) and semantic knowledge modelling (T2.3).

XAI methods and Benchmark Suite for Human-AI systems v1

Delivers the software implementations of (i) Glass-box models (T3.1), (ii) Black-box models’ interpretability (T3.2), and (iii) Benchmark Suite (T3.5).

HumAIne Platform Integration v1

Reports periodically on HumAIne platform overall implementation (T5.1, T5.2, T5.3)

Reference Architecture and Specifications v1

Documentation of the HumAIne RA (T2.6) and technical specifications. Including task and workflow modelling (T2.2) and semantic knowledge modelling (T2.3).

Use Cases Specification and Pilot Sites Preparation v1

Report on the detailed specifications of the pilots & UCs (T6.1)

Integrated Pilot Systems and Pilot Operations v1

Prototype implementation and demonstrators of all pilots/UCs

Training Centre Development for Human-AI collaboration v1

Delivers the development related to the Digital Training Center (T5.4)

Requirements Analysis, Reference Scenarios, Applicable Standards and Regulations v1

Report on the analysis of requirements, the review of the state of the art and the reference scenarios for human-AI based on T2.1. It includes analysis of the applicable standards, including information about their impact on the projects developments (T2.4).

Advanced AI Platforms for human-AI collaboration v1

Delivers the software implementations of (i) Active Learning Platform and models (T4.2), (ii) Swarm Intelligence Platform and models (T4.3), (iii) Neuro-Symbolic AI Platform and models (T4.4). It also analyses the data supported and used by the AI platforms (T4.1) and platforms’ adaptation to different requirements (T4.5).

Dissemination, Communication, and Standardization Activities v1

A report (living document) on the outcomes of T7.1, T7.2, T7.5 that will be delivered periodically. It also presents the projects training activities (T7.4).

Dissemination and Community Building Plan

Report on planning of T7.1 and T7.2.

Next Level Human Machine Interfaces 1st version available v1

Delivers the implementations of (i) Human Machine Interfaces (T3.3), (ii) Knowledge Exchange Techniques (T3.4)

Requirements Analysis, Reference Scenarios, Applicable Standards and Regulations v2

Report on the analysis of requirements, the review of the state of the art and the reference scenarios for human-AI based on T2.1. It includes analysis of the applicable standards, including information about their impact on the projects developments (T2.4).

Detailed Methodology for User Studies and Co-Creation v1

Detailed methodology for user studies and cocreation activities. (T2.6)

Data Management Plan (1)

Data Management Plan v1

The projects DMP in two versions;

Pubblicazioni

Peer reviewed articles (6)

Predicting Defect Rates of Printed Circuit Board Assemblies: Towards Zero Defect Manufacturing and Zero-Maintenance Strategies

Autori: E. Miedema, H. Kortman, C. Emmanouilidis
Pubblicato in: IFAC-PapersOnLine, Numero 58, 2024, ISSN 2405-8963
Editore: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.08.056

Neural Computing and Applications

Autori: George Fatouros; Kostas Metaxas; John Soldatos; Dimosthenis Kyriazis
Pubblicato in: Neural Computing and Applications, 2024, ISSN 0941-0643
Editore: Springer Verlag
DOI: 10.1007/S00521-024-10613-4

A Dynamic Multi-objective Scheduling Approach for Gradient-Based Reinforcement Learning

Autori: Katharina Hengel, Achim Wagner, Martin Ruskowski
Pubblicato in: IFAC-PapersOnLine, Numero 58, 2024, ISSN 2405-8963
Editore: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.09.087

Towards using behavior trees in industrial automation controllers

Autori: Aleksandr Sidorenko, Mahdi Rezapour, Achim Wagner, Martin Ruskowski
Pubblicato in: Editorial 57th CIRP International Conference on Manufacturing Systems (CIRP CMS 2024), Elsevier, 2024, ISSN 2212-8271
Editore: Elsevier, Science Direct, Procedia CIRP
DOI: 10.1016/J.PROCIR.2024.10.233

Asset criticality and risk prediction via machine learning in wind farms: problem-based educational activities in a smart industry operations course

Autori: Christos Emmanouilidis, Ype Wijnia
Pubblicato in: IFAC-PapersOnLine, Numero 58, 2024, ISSN 2405-8963
Editore: Elsevier BV
DOI: 10.1016/J.IFACOL.2024.08.119

Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection

Autori: Georgios Fatouros, Konstantinos Metaxas, John Soldatos, Dimosthenis Kyriazis
Pubblicato in: SSRN Electronic Journal, 2024, ISSN 1556-5068
Editore: Elsevier BV
DOI: 10.2139/ssrn.4693849

Conference proceedings (7)

Integrating Knowledge Graphs and Large Language Models for Querying in an Industrial Environment

Pubblicato in: Proceedings of Data Mining and Data Warehauses – Sikdd 2024, 2024
Editore: Jožef Stefan Instutute
DOI: 10.70314/is.2024.sikdd.5

Skills Composition Framework for Reconfigurable Cyber-Physical Production Modules

Autori: Aleksandr Sidorenko, Achim Wagner, Martin Ruskowski
Pubblicato in: """2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) IEEE Industrial Electronics Magazine""", 2024
Editore: IEEE
DOI: 10.1109/ETFA61755.2024.10710864

Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting

Autori: Costas Mylonas, Titos Georgoulakis, Magda Foti
Pubblicato in: 2024 International Conference on Smart Energy Systems and Technologies (SEST), 2024
Editore: IEEE
DOI: 10.1109/SEST61601.2024.10693980

Enhancing Ontology Engineering with LLMs: From Search to Active Learning Extensions

Pubblicato in: Proceedings of Data Mining and Data Warehauses – Sikdd 2024, 2024
Editore: Jožef Stefan Instutute
DOI: 10.70314/is.2024.sikdd.28

Integrating Knowledge Graphs and Large Language Models for Querying in an Industrial Environment

Autori: Hočevar, Domen and Klemen Kenda
Pubblicato in: 2024
Editore: Proceedings of Data Mining and Data Warehauses – Sikdd 2024
DOI: 10.70314/IS.2024.SIKDD.5

Enhancing Ontology Engineering with LLMs: From Search to Active Learning Extensions

Pubblicato in: Proceedings of Data Mining and Data Warehauses – Sikdd 2024, 2024
Editore: Jožef Stefan Instutute
DOI: 10.70314/IS.2024.SIKDD.28

Enhancing Ontology Engineering with LLMs: From Search to Active Learning Extensions

Autori: Kholmska, Ganna, Klemen Kenda and Jože Rožanec
Pubblicato in: Proceedings of Data Mining and Data Warehauses – Sikdd 2024, 2024
Editore: Jožef Stefan Instutute
DOI: 10.70314/IS.2024.SIKDD.28

Book chapters (2)

Integrating Knowledge and Data-Driven Artificial Intelligence for Decisional Enterprise Interoperability

Autori: Christos Emmanouilidis, Sabine Waschull, Jessica Zotelli
Pubblicato in: Communications in Computer and Information Science, Innovative Intelligent Industrial Production and Logistics, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-80775-6_26

Robust Novel Defect Detection with Neurosymbolic AI

Autori: Spyros Theodoropoulos, Georgios Makridis, Dimosthenis Kyriazis, Panayiotis Tsanakas
Pubblicato in: IFIP Advances in Information and Communication Technology, Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments, 2024
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-71637-9_26