In this talk, I’ll describe a succession of complex systems modelling within different scientific disciplines, such as financial and banking networks, immunology, chemistry, climatology, with, in all cases, the pleading for object orientation and the resorting to UML diagrams to facilitate the interfacing between the software developers and the researchers in those disciplines. In a second time, I’ll discuss the concept of emergence, its counter-scientific nature, arguing for a better investigation of the necessary ingredients contributing to a necessary de-subjectivation. For instance, in biology, three of those ingredients could allow a collective phenomenon to be labelled as “emergent.” First, the phenomenon, as usual, requires a group of natural objects entering in a non-linear relationship and potentially entailing the existence of various semantic descriptions depending on the human scale of observation. Second, this phenomenon has to be observed by a mechanical observer instead of a human one, which has the natural capacity for temporal or spatial integration, or both. Finally, for this natural observer to detect and select the collective phenomenon, it needs to do so on account of the adaptive advantage this phenomenon is responsible for.
Hugues Bersini is born the 19/1/61 and is living in Brussels. He has an MS degree (1983) and a Ph.D in engineering (1989) both from Université Libre de Bruxelles (ULB). After having been supported as a researcher by a EEC grant from the JRC-CEE in Ispra (1984-1987), he became member of the IRIDIA laboratory (the AI laboratory of ULB). He is now heading this same lab with Marco Dorigo.
Since 1992, he has an assistant professor position at ULB and has now become full professor, teaching computer science, Web technology, business intelligence, programming and AI. Over the last 25 years, he has published about 350 papers on his research work which covers the domains of cognitive sciences, AI for process control, connectionism, fuzzy control, lazy learning for modelling and control, reinforcement learning, biological networks, the use of neural nets for medical applications, frustration in complex systems, chaos, computational chemistry, object-oriented technologies, immune engineering and epistemology. He is quite often asked for giving tutorials covering the use of machine learning, object orientation and the behaviour of complex systems. IRIDIA is a pionneer in the exploitation of biological metaphors (such as the immune system and the ant colony optimisation) for engineering and cognitive sciences. Bersini is the author of fourteen french books covering basic computer sciences that have become important references in the french academic world. He teaches AI, object-oriented programming: C++, java, .Net, UML, Kotlin, Django/Pyton, Design Patterns to university students (Solvay and Polytechnic Schools) and for industries. He is consultant for companies in Object Orientation, Data-Mining technologies and Business Intelligence. These last ten years, seven spin-off have been created out of researchs done in this lab IRIDIA.
Tiziana Catarci is full professor in Computer Science and Engineering at Sapienza University of Roma (http://www.diag.uniroma1.it/users/tiziana_catarci) director of the Department of Computer, Control, and Management Engineering “Antonio Ruberti”. Moreover, she is the Editor-in-Chief of the ACM Journal of Data and Information Quality. Tiziana Catarci’s main research interests are in the hci and database areas and in the intersection between the two. She is recently working on AI ethics as a founder of SIpEIA, the Italian Scientific Society for Ethics in Artificial Intelligence. On these topics she has published over 200 papers in international journals, conferences and workshop proceedings, and 20 books (see https://dblp.org/pers/c/Catarci:Tiziana.html for a significant subset). In 2020 she has been included in the list of World's Top 2% Scientists compiled by the Stanford University (https://data.mendeley.com/datasets/btchxktzyw/2).
In her career she has received many honors, just to cite the last ones: in 2016 she has been included among the “100 Women for Science” project - http://www.100esperte.it/. In 2017 she received the Levi Montalcini Association award for the "diffusion of scientific culture among younger generations". In 2018 she has been included among the “InspiringFifty”, https://italy.inspiringfifty.org/, the most influential women in the tech world. In 2021 she received the Women&Tech international award “Le Tecnovisionarie”.
Finally, she is very active in combating gender disparities and promoting the STEM disciplines among female students by promoting dedicated projects at both school and university level and acting as role-model for “Inspiring Girls”(http://www.inspiring-girls.com/) and other initiatives.
Self-adaptation equips a computing system with a feedback loop. This feedback loop resolves uncertainties that were difficult to anticipate before the system was deployed, such as sudden changes in the availability of resources and fluctuating workloads. One of the key challenges of engineering self-adaptive systems is ensuring that the system complies with the adaptation goals, regardless of the uncertainties. A prominent approach to tackle this challenge is the use of formal approaches at runtime. In this talk we explain the basic principles of self-adaptation and zoom in on decision-making using formal approaches. We illustrate these principles for ActivFORMS (Active FORmal Models for Self-adaptation), an approach that we developed in our research group. ActivFORMS spans four main stages of the life cycle of a feedback loop: design, deployment, runtime adaptation, and evolution. We give an overview of ActivFORMS and zoom in on one of its distinct features: the use of statistical model checking of runtime models to select adaptation options that realise the adaptation goals with a required level of accuracy and confidence. We use an Internet of Things application for building security monitoring as illustrative case.
Danny Weyns is a professor at the Katholieke Universiteit Leuven, Belgium; he is also affiliated with Linnaeus University, Sweden. The research of Danny’ team is centred on the engineering of self-adaptive systems. His particular focus is on achieving trustworthiness of self-adaptive systems that operate under uncertainty. To that end, he combines runtime models that represent uncertainty as first-class citizen with verification techniques at runtime in order to provide assurances for the required adaptation goals. Validation domains include service based systems, cyber-physical systems, and the Internet of Things. Danny recently authored the book “An Introduction to Self-adaptive Systems: A Contemporary Software Engineering Perspective” that was published by Wiley.