Janusz Kacprzyk is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, WIT – Warsaw School of Information Technology, and Chongqing Three Gorges University, Wanzhou, Chinqgqung, China, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements. 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, Foreign Member of the: Bulgarian Academy of Sciences, Spanish Royal Academy of Economic and Financial Sciences (RACEF), Finnish Society of Sciences and Letters, and Flemish Royal Academy of Belgium of Sciences and the Arts (KVAB). He was awarded with 4 honorary doctorates. He is Fellow of IEEE, IET, IFSA, EurAI 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 7 books, (co)edited more than 100 volumes, (co)authored more than 600 papers, including ca. 80 in journals indexed by the WoS. His bibliographic data are: Google Scholar: citations: 28094; h-index: 74, Scopus: citations: citations: 8416; h-index: 40, ResearcherID (M-9574-2014): citations: 9014; h-index=42 and Web of Science: citations: 6696 (5517 without self-citations) h-index: 34.
He is the editor in chief of 7 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 and Past President of International Fuzzy Systems Association.
Speech Title: Automated and Quasi-automated Intelligent Decision Making: Effectiveness and Efficiency of Decision Aid
Abstract: In the human life, but also in the operation of all kinds of artificial systems meant to mimick some human capabilities, decision making is omnipresent, meant as a choice of an option(s) or a course(s) of action due to some rationality or reason. Decision making has been for centuries a subject of extremely intensive research, in many areas and in many settings exemplified by individual, group, organizational, political, managerial, consumer, etc. All these approaches and models become more and more complicated in view of increasingly complex systems decision making processes proceeds in, increasingly high data volumes available. Moreover, a higher and higher trustworthiness of analyses and solutions is required. The complexity and time criticality of the decision processes calls for the so-called automated decision making which proceeds without any human involvement, just based on data, profiles of stakeholders, as in systems for awarding loans in banks. However, such a humanless problem solving process has been found ineffective in most more sophisticated and non-trivial situation in which human judgments, preferences, intentions, etc. matter, for instance in virtually all non-trivial management problems. This has led to the so-called quasi-automated decision making in which some elements of problem solving are left to the humans. This is what we advocate. We consider complex decision making problems involving multiple agents, criteria, attributes, and also possibly dynamics. The problem formulation is as an optimization like problem, maybe solved by using a metaheuristic. Our first concern is why such problems can be difficult to solve. We advocate the so-called decision aid as an effective and efficient solution procedure. Basically, due to the complexity of the problems the decision maker (client), who is an expert in his field but not in the solution methods, commissions an analyst, who is an expert in the solution methods but not in the problem’s field. We show some examples of such a solution philosophy, e.g. in multicriteria decision making, and advocate its advantages. Since there are 3 stakeholder (client, analyst, and the pair „client-analyst”), we show some aspects related to such complex relations, notably related to the advice giving (by the analyst) and advice taking (by the client), etc. We show the effectiveness and efficiency of such an interactive decision making via decision aid, and advocate the quasi-automated decision making for complex, nontrivial problems. Finally, we mention some new proposals related to the use of recommendation and decision support systems to extend the approach .
Professor Robert Kozma
University of Memphis, TN, USA
Fellow, IEEE & INNS, Past President-INNS
Editor-in-Chief, IEEE Transactions on Systems, Man and Cybernetics: Systems
Dr. Robert Kozma (Fellow IEEE; Fellow INNS). He holds a Ph.D. in Applied Physics (Delft University of Technology, The Netherlands), two M.Sc. degrees (Mathematics, Eotvos University, Hungary; and Power Engineering, Moscow, MEI, Russia). He is Professor of Mathematics, funding Director of Center for Large-Scale Intelligent Optimization and Networks (CLION), FedEx Institute of Technology, University of Memphis, TN. Visiting positions include Professor of Computer Science, University of Massachusetts Amherst, MA; US Air Force Research Laboratory, Sensors Directorate, WPAFB, OH; NASA Jet Propulsion Laboratory, Robotics Division, Caltech, Pasadena, CA. Previous affiliations include University of California at Berkeley, EECS and Division of Neurobiology; Otago University, Information Sciences, New Zealand; Tohoku University, Quantum Science and Engineering, Japan. He has over 30 years of experience in intelligent signal processing, autonomous systems, large-scale networks, distributed sensor systems, and biomedical domains. Published 9 books/edited volumes, about 300 papers, has 2 patents. Gave over 200 presentations at conferences, about half of them are plenary, keynote, and invited talks. Research funding by agencies NASA, DARPA, AFRL, AFOSR, NSF, ONR, and others. Dr. Kozma has been the President of the International Neural Network Society (2017-2018), served on AdCom of the IEEE Computational Intelligence Society (2009-2012), and on the Governing Board of IEEE SMC Society (2016-2018, 2020), and International Neural Network Society (2004-2012). He is Editor-In-Chief of IEEE Transactions of Systems, Man, and Cybernetics - Systems. He received various awards, including the INNS Dennis Gabor Award.
Speech Title: Sustainable Artificial Intelligence
Abstract: Cutting-edge AT and Deep Learning dominates today's science and technology. In spite of the spectacular successes, advanced AI systems often require huge amount of data, energy, and computational power, which may not be readily available in various scenarios. Our computer hardware obeyed Moore's law for over half a century, but they reach an end soon demanding a drastic reformulation of existing approaches to computing. Energy constraints are often ignored or have just secondary role in typical cutting-edge AI approaches. Among the various potential solutions for sustainable AI, neuromorphic computing and chip designs gained prominence in recent years. Popular crossbar architectures are especially well suited for pattern-based computing, with the potential of complementing the sequential symbol manipulation paradigm of traditional Turing machines. Applications include autonomous on-board signal processing and control, distributed sensor systems, autonomous robot navigation and control, and rapid response to emergencies.
Speech Title: Computational Intelligence for Intelligent Healthcare Informatics
Abstract: The term "intelligent healthcare informatics" is a multidisciplinary field of research, at the intersection of medical sciences, biology sciences, biochemistry neurosciences, cognitive sciences, informatics and artificial intelligence. In the last years, various computational intelligence (CI) techniques and methodologies have been proposed by the researchers in order to develop intelligent healthcare systems (IHS) for different medical and healthcare tasks. These systems are based on the knowledge engineering paradigms and artificial intelligence concepts and theories. Many types of IHS are in existence today and are applies to different healthcare domains and tasks. In this talk we focus our discussion around the potential role of CI in developing HIS. The talk covers a wide spectrum of CI techniques and intelligent algorithmic issues, discussing implementations and case studies, assessing implementation models and practices of AI paradigms in smart healthcare systems. Moreover, the talk addresses the challenges faced by the application developers and knowledge engineers in developing and deploying such systems. In addition, the talk presents some cases of IHS developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt.