IDS-2016 Keynote Speakers

 Reza Langari

Professor of Mechanical Engineering

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Department of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX 77843

 

Biography

Dr. Reza Langari received his B.Sc., M.Sc. and Ph.D. from the University of California at Berkeley in 1981, 1983 and 1991 respectively. He has worked for Measurex Corporation, Integrated Systems, Inc., and Insight Development Corporation and has held research positions at NASA Ames Research Center, Rockwell International Science Center (RISC), United Technologies Research Center (UTRC) as well as the US Air Force Research Laboratory (AFRL). Dr. Langari’s expertise is in the area of computational intelligence with application to mechatronic systems, industrial automation and autonomous vehicles. He is the co-author of the textbook, Fuzzy Logic: Intelligence, Control and Information, Prentice Hall, 1999, Measurement and Instrumentation, Elsevier, 2011 and 2015 (2nd ed.) and co-editor of Fuzzy Control: Synthesis and Analysis, Wiley, 2000 as well Industrial Applications of Fuzzy Systems (IEEE Press, 1995.) He has served as associate editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on Vehicular Technologies as well as the ASME Journal of Dynamic Systems, Measurement and Control. He currently serves as the Editor-in-Chief of the Journal of Intelligent and Fuzzy System.

 

Human Driving Model for Autonomous Driving

Abstract

In this presentation, we consider the development of decision-making models for autonomous driving that emulate human driving. We consider lane changing in highway driving, merging maneuvers and the like where it is critical that that autonomous vehicles behave in a safe but also predictable manner. First, our investigation focuses on developing a subjective collision risk estimation model. Through the game theoretic estimation of the counterpart's behaviors and the corresponding time-evolution of the unsafe collision areas, we compute an objective collision model. In turn, we design a human-like predictive perception model for the collision with an adjacent vehicle, based on the objective collision model and the driver's subjective level of safety assurance. Next, a driving controller is designed to optimally avoid the anticipated collision for the prediction time horizon using model predictive control, which is founded on the subjective collision estimate that varies for every individual who has different aggressiveness. Simulation results indicate that the subject vehicle can react to the surrounding vehicles even without immediate actions from the counterpart. This simulates a typical driver's reasoning in view of his/her disposition so that the driver's reaction in response to roadway traffic is appropriately considered.

 

Witold Pedrycz

Professor and Chair, Canada Research Chair, IEEE Fellow, Professional Engineer

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Department of Electrical and Computer Engineering

 

Biography

Witold Pedrycz is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He is a foreign member of the Polish Academy of Sciences, and a Fellow of the Royal Society of Canada, IEEE, and IFSA. He is a recipient of numerous awards including a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, Cajastur Prize for Soft Computing, Killam Prize, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. 

His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He is an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering. 

Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences and Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley). He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals. 

 

Developing Information Granules for Intelligent Data Analysis and Decision-Making Processes

Abstract

Information granules play a pivotal role in acquiring, representing, processing, and communicating knowledge at a suitable level of abstraction. Designing information granules is central to all pursuits of Granular Computing. 

The presentation offers a comprehensive and systematically structured overview of methodologies and algorithms of designing information granules along with a suite of representative applications in data analysis and decision-making. The taxonomy embraces two main categories of data-driven and knowledge-oriented approaches. We introduce and discuss a principle of justifiable granularity, which serves as a key design vehicle facilitating a formation of information granules completed on a basis of available experimental evidence.  Recent advances of the principle are discussed including (i) a collaborative version of the principle supporting data analysis carried out in the presence of distributed data, (ii) context-based version of the principle incorporating auxiliary sources of knowledge, and (iii) its hierarchical version facilitating handling experimental evidence being available at several levels of specificity (abstraction). A collection of design scenarios supporting a formation of hierarchies of information granules of higher type and higher order is presented. 

In the realm of data analysis, we discuss a collaborative mode of discovery of relationships and a granular summarization of findings quantified in the language of information granules. We advocate the role of information granules in group decision-making highlighting a mechanism of calibration of individual decision-making models augmented by a granular knowledge transfer. We discuss matching mechanisms realized through information granules.

  

Irina Perfilieva

Professor in applied mathematics

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Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic

 

Biography

Professor Irina Perlieva, Ph.D., received the degrees of M.S. (1975) and Ph.D (1980) in Applied Mathematics from the Lomonosov State University in Moscow, Russia. At present, she is full professor of Applied Mathematics in the University of Ostrava, Czech Republic. At the same time she is Head of Theoretical Research Department in the University of Ostrava, Institute for Research and Applications of Fuzzy Modeling. She is the author and co-author of six books on mathematical principles of fuzzy sets and fuzzy logic and their applications, she is an editor of many special issues of scientic journals. She has published over 270 papers in the area of multi-valued logic, fuzzy logic, fuzzy approximation and fuzzy relation equations. She is an area editor of IEEE Transactions on Fuzzy Systems and International Journal of Computational Intelligence Systems, and an editorial board member of the following journals: Fuzzy Sets and Systems, Iranian Journal of Fuzzy Systems, Journal of Uncertain Systems, Journal of Intelligent Technologies and Applied Statistics, Fuzzy Information and Engineering. She works as a member of Program Committees of the most prestigious International Conferences and Congresses in the area of fuzzy and knowledge-based systems. For her long-term scientic achievements she was awarded on the International FLINS 2010 Conference on Foundations and Applications of Computational Intelligence. She received the memorial Da Ruan award for the best paper at FLINS 2012. In 2013, she was elected to be an EUSFLAT Honorary Member. She got a special price of the Seoul International Inventions Fair 2010. She has two patents in the area of time series processing and the Internet service technique. Her scientic interests lie in the area of applied mathematics and mathematical modeling where she successfully uses modern as well as classical approaches.

 

Fuzzy Modeling in Computer Vision

The talk will be focused on the technique of F-transform with various applications to computer vision tasks: pattern maching, image recognition up to augmented reality.

 

Vilem Novak

 

vilemtečkanovak@osutečkacz

University of Ostrava Institute for Research and Applications of Fuzzy Modeling 30. dubna 22, 701 03 Ostrava 1, Czech Republic

 

Biography

Prof. Vilém Novák, PhD, DSc. is a director of the Institute for Research and Applications of Fuzzy Modeling of the University of Ostrava, Czech Republic. He obtained PhD in mathematical logic at Charles University, Prague in 1988; DSc. (Doctor of Sciences) in computer science in Polish Academy of Sciences, Warsaw in 1995; full professor at Masaryk University, Brno in 2001. His research activities include mathematical fuzzy logic, approximate reasoning, modeling of linguistic semantics, fuzzy control and various kinds of applications of fuzzy logic. He is one of the founders of mathematical fuzzy logic. He was general chair of the VIIth IFSA'97 World Congress in Prague, June 25{29, 1997 and also of 5th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2007 Ostrava), Ostrava, Czech Republic, September 11-14, 2007. He is member of editorial boards of several journals (Soft Computing, Fuzzy Sets and Systems, International Journal of Computational Intelligence Systems, and several other ones) and guest editor of several special issues of these journals. He is author or co-author of over 260 scientic papers, 5 monographs and 2 edited monographs. He was awarded in the International FLINS 2010 Conference on Foundations and Applications of Computational Intelligence for his scientic achievements. A list of his publications and further information can be found in the WEB page http://irafm.osu.cz/

 

Forming Interpretable Systems using Fuzzy Natural Logic 

Abstract

Fuzzy natural logic (FNL) is a theory that provides mathematical models of special human reasoning schemes that employ natural language including its typical feature which is vagueness of its semantics. The models should be independent of a concrete language as much as possible. On the other hand, by a fuzzy system we understand a mathematical model of some real phenomenon or process that is formed using degree-oriented methods in which the concept of fuzzy set plays an essential role. In the talk, we will argue that methods of FNL are suitable for formation of well interpretable fuzzy systems.

First, we will briefly overview the main concepts of FNL, namely the concepts of evaluative linguistic expressions, fuzzy/linguistic IF-THEN rules, linguistic descriptions and linguistic quantifiers. Then we will explain the principle of methods for finding conclusions on the basis of expert information formed in natural language that mimic the human way of reasoning. Finally we will demonstrate how interpretable fuzzy systems can be formed in applications to control, decision-making, formation of linguistic associations, and analysis and forecasting of time series.

 

Bernard De Baets

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Ghent University

 

Biography

B. De Baets holds an M.Sc. degree in Mathematics (1988), a Postgraduate degree in Knowledge Technology (1991) and a Ph.D. degree in Mathematics (1995), all summa cum laude. He is a Full Professor (2008) in Applied Mathematics at Ghent University, Belgium, where he is leading the research unit Knowledge-based Systems (KERMIT, 2000) at the Faculty of Bioscience Engineering. He is an affiliated professor (2009) at the Anton de Kom Universiteit (Suriname) and an Honorary Professor (2006) of Budapest Tech (Hungary). He was elected Fellow of the International Fuzzy Systems Association in 2011 and has been nominated for the 2012 Ghent University Prometheus Award for Research.

KERMIT is an interdisciplinary team of (bio-) engineers, computer scientists and mathematicians. Its current activities consist of three interwoven threads: knowledge-based, predictive and spatio-temporal modelling. B. De Baets has acted as supervisor of 53 Ph.D. students. At present, almost 30 Ph.D. students are involved in the research activities of KERMIT, either in-house, through affiliations or in the framework of joint projects. Due to its unique position, KERMIT serves as an attraction ole for applications in the applied biological sciences.
The bibliography of B. De Baets comprises about 400 publications in international peer-reviewed journals, 60 chapters in books and 300 contributions to proceedings of international conferences. He delivered over 200 lectures at conferences and research institutes. He has received several best paper awards. B. De Baets is co-editor-in-chief (2007) of Fuzzy Sets and Systems and member of the editorial board of several other journals.

 

Decision making in the presence of cycles

Abstract

Reciprocal relations are an interesting generalization of complete relations covering both an important class of preference relations studied in fuzzy set theory, as well as the class of winning probability relations studied in probability theory. Due to their intrinsic nature, reciprocal relations that are not weakly transitive can be considered to be cyclical. Most attention so far has gone to cycles of lengths three, using the metaphor of the Rock-Paper-Scissors children game, triggered by evidence from nature and society.

In this lecture, I will focus on winning probability relations associated with random vectors and point out the link with the underlying dependence structure. Interesting prototypical settings, such as dice games, mutual rank probability relations and graded stochastic dominance, will be discussed. Ample links to applications in population dynamics, chemometrics, social statistics and machine learning will be provided.