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 Janusz Kacprzyk – Radu-Emil Precup – Viviana Mascardi – Piotr Kowalski

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Prof. 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 and Lithuanian Academy of Sciences. He was awarded with 8 honorary doctorates. He is Fellow of IEEE, IET, IFSA, EurAI, IFIP, AAIA, 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 650 papers, including ca. 150 in journals indexed by the WoS. He is listed in 2020, 2021, 2022 ”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 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 and Past President of International Fuzzy Systems Association.

Title: An approach to Consensus and dissensus driven decision making under fuzziness

 

Abstract:

We advocate a new way of decision making under fiuzzy preferences and fuzzy majorities the essence of which is that the consensus driven paradigm, which is very often employed, is not followed as it can often prohibit reaching innovative decisions.

First, we briefly review main developments in the broadly perceived group decision making and indicate the main general approaches, namely via: 1) Unanimous decisions when all agents agree without reservations; 2) Consensus in which each agent agrees to give his/her consent to the decision reached, even if it would not be perfect, but acceptable, and is open to modify his or her testimonies; 3) Majority Rule when, e.g., more than a half, at least 2/3, etc, of the group votes in favor; 4) Expert in which a special agent, an expert, is chosen for running the decision making process; 5) Executive in which a special high lvel agent makes the decision with little or none involvement of other group members; 6) Default in which a decision is made as needed, without any analysis.

We analyze some new directions in social sciences, cognitive scence, psychoiogy, decision theory, etc. in which there is an explicit critique of consensus as a viable and effective and efficient way of making group decisions. Basically, the argument is that „consensus is the quickest way to kill innovation”. This arguent is raised by many authors, and there are some attampts to use some more formal analyses.

We also use some results of the so called entrepreneurial action theory which also mentions that the most interesting forms of entrepreneurship involve ideas that contradict prevailing wisdom, and opinions. These behaviors may be irrational  and seem impossible but may lead to innovative, even revolutionary outcomes.

We propose a new model of dissensus driven group decision making under fuzzy preferences and fuzzy majorities. We redefine the solution concepts along the line of the fuzzy cores and fuzzy consensus winners. We mention an examples.

 

Radu-Emil Precup

Prof. Radu-Emil Precup is currently with the Politehnica University of Timisoara (UPT), Romania, where he became a Professor in the Department of Automation and Applied Informatics, in 2000, and he is currently a Ph.D. supervisor in automation and systems engineering. Since 2022 he is also a senior researcher (CS I) and the head of the Data Science and Engineering Laboratory of the Center for Fundamental and Advanced Technical Research, Romanian Academy – Timisoara Branch, Romania. From 2016 to 2022, he was an Adjunct Professor within the School of Engineering, Edith Cowan University, Joondalup, WA, Australia. He is currently the director of the Automatic Systems Engineering Research Centre of the UPT. From 1999 to 2009, he held research and teaching positions with the Université de Savoie, Chambéry and Annecy, France, Budapest Tech Polytechnical Institution, Budapest, Hungary, Vienna University of Technology, Vienna, Austria, and Budapest University of Technology and Economics, Budapest, Hungary. He has been an associate editor of IEEE Transactions on Fuzzy Systems (2018-2022), and serves on the editorial boards of several prestigious journals, including IEEE Transactions on Cybernetics, Information Sciences (Elsevier), Engineering Applications of Artificial Intelligence (Elsevier), Applied Soft Computing (Elsevier), Expert Systems with Applications (Elsevier), Evolving Systems (Springer), Applied Artificial Intelligence (Taylor & Francis), Healthcare Analytics (Elsevier), and Communications in Transportation Research (Elsevier).

Prof. Precup is a corresponding member of the Romanian Academy, a Doctor Honoris Causa of the Óbuda University, Budapest, Hungary, and a Doctor Honoris Causa of the Széchenyi István University, Győr, Hungary. He received the Elsevier Scopus Award for Excellence in Global Contribution (2017), was named a 2022 academic data leader by Chief Data Officer (CDO) Magazine, and was listed as one of the top 10 researchers in artificial intelligence and automation (according to IIoT World as of July 2017).

Title: Applications of metaheuristic algorithms to fuzzy control and model building, learning-based control, and mobile robot navigation

Abstract: An optimization problem finds the best (i.e., optimal) solution among all feasible solutions. An optimization problem consists of two key components: the objective function and the constraints, which are optional. The objective function evaluates and compares solutions in the context of all feasible solutions by computing the desired quantity to be minimized or maximized. Constraints can be added to limit the possible values for the variables of the objective function and possibly to link these variables.
The optimization algorithms find the solutions to the optimization problems (i.e., the optimal solutions) by trying variations of the initial solution and using the information gained to improve the solution. This solution finding can also be considered as learning, which is a popular topic nowadays. The complexity of classical algorithms is very high, which requires rather large amount of computation. Therefore, alternative algorithms with lower complexity are appreciated. Metaheuristic algorithms for finding optimal solutions have become very popular because they are much better in terms of efficiency and complexity than classical algorithms.
This presentation highlights some of the results obtained by the Process Control Group of the Politehnica University of Timisoara, Romania. The presentation will focus on representative applications implemented in our labs, with real-time validation against experimental results. The results highlighted here include various laboratory equipment such as pendulum crane systems, multi-tank systems, servo systems, twin rotor aerodynamic systems, magnetic levitation systems, anti-lock braking systems, mobile robots, magnetic levitation systems, active mass damper systems, and shape memory alloy systems.
The scope of development of these metaheuristic algorithms is to solve optimization problems involving tuning of low-cost fuzzy controllers, tuning of fuzzy models, reinforcement-based control in various schemes including adaptive ones, and solving optimization problems specific to mobile robot navigation.

This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number ERANET-ENUAC-e-MATS, within PNCDI IV.

 

 

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Viviana Mascardi

Prof. Viviana Mascardi is Associate Professor in Computer Science at the University of Genova, Italy, DIBRIS (Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi). She graduated cum laude at the University of Genova in 1996 and she got her PhD in Computer Science from the same University in 2002. In 2015 she became associate professor (professore di 2a fascia) and in April 2021 she got the «abilitazione al ruolo di professore di 1a fascia nel settore INF/01» (habilitation for the full professor in Computer Science role).

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Piotr Kowalski

Prof. Piotr A. Kowalski holds the position of Professor at the AGH University of Science and Technology, working at the Faculty of Physics and Applied Computer Science, as well as at the Systems Research Institute of the Polish Academy of Sciences. He earned his Master’s degree in Teleinformatics and Automatic Control (both with honours) from the Cracow University of Technology in 2003, followed by a Ph.D. in Data Science from the Polish Academy of Sciences in 2009. In 2018, he achieved the D.Sc. (habitation) degree in Artificial Neural Networks at the Systems Research Institute of the Polish Academy of Sciences. In 2019, he was appointed as a University Professor at AGH University of Science and Technology in Krakow.

His research interests lie in the field of information technology, mainly focusing on intelligent methods such as neural networks, fuzzy systems, and nature-inspired algorithms, applied to complex systems and knowledge discovery processes. From 2018 to 2023, he served as a member of the management group and led the conference grant for young scientists within Cost Action 17124 DigForAsp (Digital forensics: evidence analysis via intelligent systems and practices), funded by the European Cooperation in Science and Technology (COST). Additionally, he has been actively involved in various research and development projects, including those funded by the Ministry of Science, the National Centre for Research and Development, and the Małopolska Centre for Entrepreneurship.

Piotr A. Kowalski is a member of the Polish Information Processing Society and the Institute of Electrical and Electronics Engineers, particularly the IEEE Computational Intelligence Society. Currently, he serves as an editor and a member of the editorial board for several scientific journals, and he is a member of the scientific committee for numerous prestigious scientific conferences. Furthermore, he is a member of the Discipline Council for Information and Communication Technology at AGH and the Scientific Council of NASK PIB. Additionally, he is a reviewer of numerous academic nominations (PhD, DSc), research grants and scientific articles, contributing his expertise to assessing scientific achievements in various fields.

Title: Sensitivity Analysis as a Method for Explaining AI (XAI) in the Artificial Neural Networks Domain

Abstract: The field of Explainable Artificial Intelligence (XAI) has gained prominence as the adoption of complex models, such as Artificial Neural Networks (ANNs), continues to grow. Sensitivity Analysis emerges as a pivotal method within the XAI framework, offering a systematic approach to unravelling the intricate inner workings of ANNs. This invited talk delves into the significance of Sensitivity Analysis as a method for explaining AI in the domain of Artificial Neural Networks. The presentation explores how sensitivity analysis techniques contribute to transparency, interpretability, and trust in AI systems, shedding light on the factors that influence model predictions. Through illustrative examples and case studies, the talk aims to provide valuable insights into the practical application of sensitivity analysis, bridging the gap between the complex nature of ANNs and the need for comprehensible AI systems.
The talk will showcase both global and local approaches of Sensitivity Analysis, providing a comprehensive understanding of how these methods can be employed to dissect the decision-making processes within ANNs. Attendees will gain insights into the application of Sensitivity Analysis to various types of neural networks, including Multilayer Perceptrons (MLPs), Probabilistic Neural Networks (PNNs), and Convolutional Neural Networks (CNNs). Through illustrative examples and case studies, the presentation aims to demonstrate the versatility of sensitivity analysis in dissecting the complex structures of different neural network architectures.
By focusing on specific neural network models, namely MLPs, PNNs, and CNNs, the talk will highlight the adaptability of sensitivity analysis across diverse AI applications. The presentation underlines a deeper understanding of how global and local sensitivity analysis techniques can enhance the interpretability and explainability of ANNs, contributing to the responsible deployment of AI systems.

 

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