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), 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 and Accademia Nazionale di Scienze, lettere e Arti (Palermo). He was awarded with 8 honorary doctorates. He is Fellow of IEEE (Life), IET, IFSA, EurAI, IFIP, IAITQM, AAIA, AIIA, I2CICC, 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 150 volumes, (co)authored more than 690 papers, including ca. 150 in journals indexed by the WoS. He is listed in 2020-2024 ”World’s 2% Top Scientists” by Stanford University, Elsevier (Scopus) and ScieTech Strategies and published in PLOS Biology Journal.
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.
Complex problem solving in human-centric and value-centric smart evironments: AI- assisted and AI-enabled approaches.
Smart environments, which have recently attracated much attention, are basically meant as „small worlds” that constitute of a collection of sensors, computers and humans – both individuals and social groups of various size, even the society as a whole – who are synergistically integrated to perform some tasks for the benefit of the stakeholders who are here assumed to be humans: individuals, social groups, enterprises, institutions, organizations or even the society as a whole. It is clearly very difficult to solve any problems in such environments because the human beings are key stakeholders. The goal is here to devise some automated agents, e.g. algorithms, to support the humans to better develop, plan, solve and implement actions, strategies and policies. The smart environment provides here various sensors, analyzers, controllers, etc., and they are pervasive.
Traditionally, the smart environments are considered in a techno-centric perspective, just with IoT sensors and other inanimate tools and techniques though the human being is the key player (stakeholder, actor) in virtually all smart environment contexts. This implies a necessity to explicitly include in research, development and implementations an active and proactive involvement of the human being. Some important aspects concern then a proper balance between the egocentric, human (individual)-oriented, and (social) value oriented views. One can use here some elements of the human-in-the-loop and society-in-the-loop paradigms.
To implement these human-centricity and value (social)-centricity within decision making processes in smart environments, artificial intelligence (AI) can help provide tools and techniques to develop new decision making and reasoning models via the so-called AI-assisted perspective which can then be implemented via an AI-enabled decision support system. Such decision support systems provide an advanced AI-assisted/based/enabled human–computer interface (HCI), use advanced AI-specific models for data analyses and handling, simulation, explanation, argumentation, etc. However, they imply many problems inherent in a human-computer cooperation/collaboration, e.g. related to trust, transparency, etc.
An example of the planning of critical sustainable insfrastructure are shown.
Website developed by Bits & Bytes Lab
©Copyright ICSEGT2025. All rights reserved
