Title
Towards an effective and efficient reaching of „good consensus” in animate and animate multiagent systems

Abstract
We are generally concerned with how consensus reaching, considered to be a crucial element of decision making, proceeds in animate (humans, to be more specific) and inanimate (e.g., robots, cobots, bots, pieces of software, etc.) systems. First, we discuss main differences between consensus in animate (human) systems implied by emotions, biases, subjective opinions or judgments, and consensus in inanimate multiagent systems which is usually data driven, algorithm driven, and follows more or less strict protocols and rationality. We indicate main implications as to mathematical formalisms, and tools and techniques to be employed. Basically, we use as the testimonies of agents to be preferences but we also add remarks on an utility based setting.
For both the animate and inanimate multiagent systems we assume the multistage consensus reaching process in which agents should be commited to consensus so that they can accept changes of their testimonies, though to some limited extent as it implies a „cost”, and such a adjustment process is repetitive. We show some models of how to develop in this context models for an effective and efficient, also fair and equitable, adjustment process. We are mostly concerned with some „softer” models exemplified by those based on fuzzy logic and possibility theory, statistics, evolutionary computation, etc. We mainly assume the moderator based consensus reaching model which is common in many animate multiagent systems, and also menyion some solutions for inanimate systems in this respect. We briefly discuss first, informally, some views of what a „good or bad consensus” is both in inanimate (every agent quickly agrees on a good option, for the good consensus) and animate (either no agreement of agreement on a bad option, for the bad consensus). We discuss some effectiveness and efficiency measures exemplified by a degree (extent) for consensus, variance, convergence time and rate, accuracy, robustness, resilience, costs of changes, compoutational efficiency, etc.
We briefl mention the importance of solving the consensus vs. innovation dillemma as too much consensus can imply less creativity (i.e. less innovation) but too much innovation can imply creativity that can be not agreed upon.
Finally we give some illustrative example of a consensus reaching process using a decision support system (DSS) for city planning.

Biodata
Janusz Kacprzyk is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, WIT – Warsaw School of Information Technology, AGH University of Science and Technology in Cracow, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements in Warsaw, Poland. He is Honorary Foreign Professor at the Department of Mathematics, Yli Normal University, Xinjiang, China. He is Full Member of the Polish Academy of Sciences, Member of Academia Europaea, European Academy of Sciences and Arts, European Academy of Sciences, International Academy of Systems and Cybernetics (IASCYS), National Academy of Artificial Intelligence (NAAI), Foreign Member of the: Bulgarian Academy of Sciences, Spanish Royal Academy of Economic and Financial Sciences (RACEF), Finnish Society of Sciences and Letters, Flemish Royal Academy of Belgium of Sciences and the Arts (KVAB), Russian Academy of Sciences. National Academy of Sciences of Ukraine, Lithuanian Academy of Sciences, Accademia Nazionale di Scienze, lettere e Arti (Palermo. Italy), and African Academy of Sciences. He was awarded with 8 honorary doctorates. He is Fellow of IEEE (Life), IET, IFSA, EurAI, IFIP, AAIA, AIIA, I2CICC, CORE, and SMIA.
His main research interests include the use of modern computation computational and artificial intelligence tools, notably fuzzy logic, in systems science, decision making, optimization, control, data analysis and data mining, with applications in mobile robotics, systems modeling, ICT etc.
He authored 8 books, (co)edited more than 150 volumes, (co)authored more than 700 papers, including ca. 150 in journals indexed by the WoS. He is listed in 2020 - 2025 ”World’s 2% Top Scientists” by Stanford University, Elsevier (Scopus) and ScieTech Strategies, and in Meta’s ScholatGPS 2024 Highly ranked scholars.
He is the editor in chief of 8 book series at Springer, and of 2 journals, and is on the editorial boards of ca. 40 journals. He is President of the Polish Operational and Systems Research Society, Past President of International Fuzzy Systems Association, and is a member of the Adcom (Administrative Committee) of the Computational Intelligence Society of the IEEE, and a member of the Board of Governors of the Systems, Man and Cybernetics Society of the IEEE.

Title
The convergence of Computational Intelligence and eHealth for improving the quality of life

Abstract
Computational intelligence (CI) has emerged as a transformative paradigm in eHealth, enabling intelligent, adaptive, and data-driven solutions to address complex healthcare challenges. By integrating techniques such as machine learning, deep learning, evolutionary computation, and fuzzy systems, CI-driven eHealth applications support personalized care, early disease detection, remote monitoring, and clinical decision support. These technologies leverage heterogeneous health data from electronic health records, wearable sensors, and mobile health platforms to generate actionable insights that enhance prevention, diagnosis, treatment, and long-term disease management. As a result, patients benefit from improved accessibility to healthcare services, increased autonomy in self-management, and more timely and precise interventions, while healthcare providers achieve greater efficiency and quality of care. Despite challenges related to data privacy, interoperability, ethical considerations, and model interpretability, ongoing advances in computational intelligence continue to strengthen the reliability and scalability of eHealth systems. This convergence of CI and eHealth holds significant potential to improve overall quality of life by promoting proactive, patient-centered, and sustainable healthcare delivery.
The Computational Biomedicine Laboratory http://www.cbml.ds.unipi.gr has developed integrated homecare solutions incorporating communication functionalities such as WebRTC, wearable devices, assistive environments and intelligent data processing modules for supporting chronic patients and seniors’ independent living and allow the continuous monitoring of their health status. Furthermore, we have extended their utility beyond conventional monitoring, by introducing affective computing capabilities would allow for early detection of potentially dangerous situations, as an individual’s emotional state has a direct effect on their health, cognitive status, behavior and quality of life. The lab, being also consistent with the latest advancement in the field of AI, has shown progress in analyzing explainability techniques with reference to medical imaging classification tasks and proposing improvements on well-established explainability algorithms. The provision of visual explanations that demonstrate the most influential areas of the image concerning the classification result is of major importance to decision making systems. The importance grows exponentially when referring to Computational Intelligence in the healthcare domain. By developing explainable systems by design or even as post-hoc approaches, trustworthiness and transparency are the added values that can bring domain experts closer to the AI paradigm. In this talk, we will present the utilization of these technologies in various cases (i.e holistic health management, chronic patients, COVID-19, patients with mental diseases, medical image analysis, stress management etc), focusing on their advanced capabilities and limitations.

Biodata
Dr. Ilias Maglogiannis is Professor in the Dept of Digital Systems in the University of Piraeus and Director of Computational Biomedicine Lab (www.cbml.ds.unipi.gr). He has been principal investigator in many European (i.e. Horizon Europe: AI4WORK, MELIORA, H2020: PolicyCloud, GATEKEEPER, CROWDHEALTH, AGILE, UNCAP, FP7: e-LICO, INHOME, FP6: UNITE, NOMAD, TELEMED, FP5: MOMEDA, INTRACLINIC) and National Research programs, while he has also served as external evaluator in R&D projects for the EU, the Government of Hong Kong, France, Portugal, Czech, Cyprus and Greece. His scientific interests include Biomedical Informatics, Machine Learning and Computer Vision, Multimedia Processing and Pervasive Healthcare Systems. His published scientific work includes three (3) books (Springer, IOS press and Morgan Claypool Publishers), 150 journal papers and more than 250 international conference papers. Dr. Maglogiannis has received more than 11000 citations on his published work (h-index = 48). He served as Associate Editor for the Journals IEEE Biomedical Health Informatics, IEEE Transactions on Information Technology in Biomedicine, Journal on Information Technology in Healthcare, and he is editorial board member of Personal and Ubiquitous Computing, Healthcare Engineering, and Intelligent Decision Technologies. He has also served as guest editor in 8 international journals (IEEE Engineering in Medicine and Biology, Oncology Reports, Simulation, Applied Intelligence, Personal and Ubiquitous Computing, Journal Universal Access in the Information Society, Neurocomputing, Evolving Systems). Dr. Maglogiannis is a Senior member of IEEE, SPIE, ACM etc and served also as affiliated faculty in the CS Dept Univ. of Texas at Arlington USA. Dr. Maglogiannis is also since 2014 president of IFIP Working Group WG12.5 (AI Applications) and Vice Chair of IEEE EMBS Greek chapter. Finally, he is elected as fellow member of EAMBES, European Alliance for Medical and Biological Engineering Sciences (http://eambes.org/) and he is included in the list of the world's top 2% scientists per scientific field as released by Stanford University, as well as in the list of the leading scientists in Computer Science in Greece according to the Research organization https://research.com/.

Title
Quantum Discrete Optimization

Abstract
I will present an overview of quantum discrete optimization, focusing on both its theoretical foundations and practical implementation on contemporary quantum hardware. It highlighting the potential to address combinatorial optimization problems that are computationally challenging for classical algorithms. Discrete optimization problems are formally defined and subsequently reformulated in a manner suitable for quantum processing, with particular emphasis on the Quadratic Unconstrained Binary Optimization (QUBO) framework.A significant part is devoted to the quantum annealing paradigm, as realized by D-Wave quantum computers, as well as quantum-gate based computers with Quantum Approximation Optimization Algorithm (QAOA). The structure of qubits, their physical implementation, and the connectivity of the D-Wave and IQM processors are discussed to illustrate the constraints and opportunities imposed by real quantum devices. Methods for embedding discrete optimization problems onto the quantum annealer are described, along with practical considerations such as problem scaling and noise.

Biodata
Wojciech Bożejko is a Professor at Wrocław University of Technology. He obtained M.Sc. in University of Wroclaw, Institute of Computer Science in 1999, Ph.D. in Wrocław University of Technology, Institute of Engineering Cybernetics in 2003, D.Sc. (habilitation) in Wrocław University of Technology, Faculty of Electronics, in 2011 and Full Professor title in 2020.From 2019 he is the Head of the Department of Control Systems and Mechatronics in Wrocław University of Science and Technology.  
His research work is related, on the one hand, to the theoretical aspects of discrete optimization, especially in the aspect of quantum and multiprocessor computing, and, on the other hand, to their practical application in effective algorithms for solving real problems. He is an author of over 260 papers (over 1200 citations on Scopus, h-index: 18  published in peer-reviewed journals and conference proceedings from the field of quantum optimization, parallel processing, scheduling and logistics. 
He has realized projects for the National Science Centre (NCN), the National Centre for Research and Development (NCBiR), and Toyota Motor Manufacturing Europe; he has served as an expert in project evaluations for the National Centre for Research and Development (NCBiR) and the Swiss National Science Foundation (SNF). He has supervised six doctoral dissertations (currently supervising two doctoral students). He is a member of the scientific societies: IEEE (senior member), IFAC, PTZP, WTN, and POLSPAR. He currently serves as the Chairman of the Scientific Discipline Council of Information and Communication Technology at the Wrocław University of Science and Technology. His Erdős number is 4. He is also a qualified musician, graduated (Master of Arts degree) in 1998 from the Academy of Music in Wroclaw in a specialization of piano.

Title
Engineering Service-based Information Systems with LLMs

Abstract
The current evolution of AI, and of Generative AI in particular, namely Large Language Models (LLMs), makes it possible to adopt them as supporting tools for the engineering of information systems, in particular for their design and development based on service-based approaches. In this keynote we will present, with an IS engineering attitude, recent approaches and applications for LLMs' usage during the design of service-based systems. Very recent works (in the last year) are emerging on how to use LLMs for the conceptual design, the software development, and extraction of business process / orchestration specification from documents. A unifying framework and pipeline will be presented, and case studies, based on the presenters’ research activities.

Biodata
School of Information Engineering, Computer Science and Statistics of Sapienza Universita di Roma, in the Department of Computer, Control and Management Sciences & Engineering ANTONIO RUBERTI (DIAG, formerly DIS – Department of Computer and Systems Sciences)