Tutorial on
Ontologies for Digital Twins
Instructors
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Romain Chassagne
BRGM
France
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Brief Bio
Romain Chassagne holds a PhD in applied mathematics and currently works at BRGM. He brings 14 years of experience in modeling and data assimilation, gained through roles in academia, as a research assistant professor, and in industry as a research scientist. His work spans various topics in geosciences (geothermal, CO2 storage, minerals, hydrogeology). Romain coordinates and leads tasks in several national research projects and serves as work package leader in two Horizon Europe. He is also an associate editor for the journal Computers & Geosciences. In his institution he coordinates digital twin dedicated program and serves as a member in different scientific committees in academia and industry.
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Fatma Chamekh
BRGM
France
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Brief Bio
Fatma Chamekh holds PHD in computer science at Lyon University. Her studies are focused on semantic web and symbolic IA for intelligent systems. He gets 10 years experience as professor assistant and research assistant in knowledge engeneering. Actually, she works on BRGM. Her areas of interest are: semantic web, knowledge reasoning, NLP, knowledge graph, LLM, digital Twin.
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Abstract
A digital twin (DT) is a digital model of an intended or actual real-world physical system (or systems), that serves to predict, to understand or to simulate a what-if scenario. DTs are used in several contexts and fields, such as business, healthcare, manufacturing, agriculture, natural risks, and the management of the subsurface. There are different contexts and approaches to designing DTs, without clear consensus on specific engineering processes and workflows. There are some gaps in the data and uncertainty inducing flaw representation through digital twin. Ontologies are a promising way to structure data and flow.
Keywords
digital twins, simulation, ontologies
Aims and Learning Objectives
The goal of this Tutorial is to provide the audience with an understanding of the importance of the semantic layer for digital twins. We present the main components (logical and physical) of digital twins among several examples coming from research projects.
Target Audience
Researchers and Industrials
Prerequisite Knowledge of Audience
None
Detailed Outline
Digital twin: definition and principals
Digital Twin: logical component
Digital Twin: physical component
The ontologies for digital twins
Conclusion and future direction
Tutorial on
Trustworthy Autonomous Systems: Challenges and Enablers
Instructor
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Joanna Isabelle Olszewska
University of the West of Scotland
United Kingdom
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Brief Bio
Dr Joanna Olszewska BSc(Hons) MSc(EPFL) PhD(UCL) CEng CSci FBCS FHEA is a British Computer Scientist. She is an Asst. Professor with UWS, UK, and leads research in Algorithms and Softwares for Trustworthy Intelligent Vision Systems. Senior Member of IEEE, she stands on the IEEE AI Standard Committee and she is part of the IEEE Global Initiative for Ethical Considerations in AI and Autonomous Systems. She is Co-Chair of the IEEE RAS Technical Committee on the Verification of Autonomous Systems. ACM Distinguished Speaker, she has participated in panels about the Future of AI and standardization efforts in robotics and autonomous systems (e.g. at ICRA). She has delivered invited talks (e.g. at the Canadian Mathematical Society, ENS Paris, DDD Scotland Industry forum), keynote talks (e.g. at SBESC), tutorials at conferences (e.g. at ICAART), webinars (e.g. at IEEE) as well as podcasts (e.g. at BCS) and interviews (e.g. at BBC). She has been TPC member of over 100 international conferences (e.g. IJCAI) and has chaired over 70 webinars/seminars/workshops (e.g. IROS). She has served as Technical Program Chair of the IEEE International Conference on Engineering Reliable Autonomous Systems (ERAS). She has been appointed Guest Editor for the Knowledge Engineering Review Journal, Cambridge University Press, and Associate Editor for the Frontiers in Artificial Intelligence journal, the Engineering Applications of Artificial Intelligence journal, and the Machine Learning with Applications journal, Elsevier. Fellow of the British Computer Society, Chartered Engineer, Chartered Scientist, and Fellow of StandICT.eu, she has contributed to 20+ ISO/IEC/IEEE standards in various roles, e.g. Vice-Chair of ISO/IEC/IEEE 41062. She has published 150+ papers and one book – ‘Artificial Intelligence and Software Testing: Building systems you can trust’ – winner of the Independent Press Award.
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Abstract
With Artificial Intelligence (AI) technologies becoming ubiquitous in people’s daily life, trustworthiness in these intelligent and autonomous systems is crucial. For this purpose, this tutorial will outline the major challenges of trustworthy autonomous systems, while it will highlight the enablers to develop and deploy such systems. In particular, the tutorial will cover methods and guidelines for designing, developing, testing and verifying autonomous systems. Furthermore, this tutorial will present IEEE and ISO/IEC/IEEE standards for the design and development of autonomous systems so they meet not only technical but also societal expectations – leading thus to the trustworthiness in autonomous systems.
Keywords
Trustworthy Autonomous Systems (TAS), Interpretable and Explainable Artificial Intelligence (XAI), Artificial Intelligence Testing, Verification of Autonomous Systems (VAS), Ontologies for Ethical AI, Artificial Intelligence (AI) and Robotics Standards, Software and System Reliability, Autonomous Systems, Intelligent Systems, Knowledge-Based Systems, Decision-Support Systems, Expert Systems, Recommender Systems, Human-AI Collaboration, Human-Robot Interaction, Robotic Systems, Intelligent Vision Systems.
Aims and Learning Objectives
1. The tutorial aims to present technical approaches to develop trustworthy autonomous systems.
2. The tutorial aims to increase awareness about the social, ethical and legal aspects as well as the standardization and ontological efforts in the context of the development and deployment of trustworthy autonomous systems.
3. The tutorial aims to provide real-world case studies and examples of developed and deployed trustworthy autonomous systems.
Target Audience
Stakeholders from Academia and Industry as well as regulators from Governmental and Professional Bodies
Prerequisite Knowledge of Audience
Suitable for students and scholars of different disciplines who are interested in the study, analysis, design, modelling and implementation of interpretable and explainable AI systems
Detailed Outline
The proposed tutorial is planned as a half-day event (morning or afternoon) including 2 segments of presentation (1 hour and 20 minutes each, with 20 minutes break between them), as follows:
- The first segment of tutorial (1 hour and 20 minutes) will be devoted at first to an introduction to the Trustworthy Autonomous Systems (including the Definition of Trustworthy Autonomous Systems as well as the Technical and Societal Challenges of Trustworthy Autonomous Systems) followed by a detailed presentation of the Key Enablers of Trustworthy Autonomous Systems and the Guidelines, Standards, and Ontologies for AI, Robotics, Autonomous and Intelligent Systems (AIS).
- The second segment of tutorial (also 1 hour and 20 minutes) continues the presentation of the Key Enablers of Trustworthy Autonomous Systems and in particular will detail the Processes ,Techniques and Ontologies to Design, Test, Verify and Assess Trustworthy Autonomous Systems. Then, the tutorial will present and discuss the Applications of Trustworthy Autonomous Systems in context of smart manufacturing and smart cities as well as trustworthy system-human interaction. This will take 50 minutes (to 1 hour). Finally, the rest of the second segment of tutorial will be devoted to the interaction with audience, i.e. discussion with audience, answer to the audience’s questions, remarks and/or specific points.