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Plenary Speaker

Witold Pedrycz (IEEE Fellow, 1998)

Professor and Canada Research Chair (CRC) in Computational Intelligence in the

Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.

New Avenues of Modeling and Simulation with

Information Granules and Granular Computing

Abstract

Models, prediction tasks, control algorithms commonly encountered in engineering, industrial environment, and simulation studies are inherently numeric artifacts coming with a long and successful tradition. These artifacts are designed on a basis of experimental data that are central to numerous pursuits. In contrast, Artificial Intelligence (AI) is deeply rooted in symbols. Knowledge representation and ensuing symbolic processing are crucial to the constructs encountered there.  Casting AI in the setting of data-rich environment calls for bridging an acute gap between symbols and data, grounding symbols and facilitating processing at suitable levels of abstraction.

Concepts constitute a concise manifestation of key aspects and properties of data. As being built at the higher (and usually adjustable) level of abstraction than the data themselves, they capture the essence of the data and usually emerge in the form of information granules. We identify three main ways in which concepts are encountered and characterized: (i) numeric, (ii) symbolic, and (iii) granular. Each of these views come with their advantages and become complementary to a significant extent.

The numeric aspects of concepts are conceptualized by engaging various clustering techniques where these methods deliver suitable algorithmic prerequisites. The quality of numeric concepts evaluated at the numeric level is described by a reconstruction criterion. The symbolic description of concepts, which is predominant in the realm of AI and symbolic computing, can be represented by sequences of labels (integers). In such a way main qualitative aspects of data are captured. This facilitates further qualitative analysis of concepts and constructs involving them by reflecting the bird’s-eye view of the data and relationships among them. They come hand in hand with a variety of analyses concerning constructs involving symbols, namely stability, distinguishability, redundancy, and conflict. All of those become central to the important facet of interpretability (transparency) of the constructs and produced outcomes.

The granular concepts augment numeric concepts by bringing information granularity into the picture and invoking the principle of justifiable granularity in their construction.  We elaborate on the general scheme of processing of granular modeling dwelling upon a collection of granular concepts and forming a collection of granular models. We also advocate the general scheme: data à representatives à symbols à linguistic summarization and show its parallel manifestation and realization through the ensuing modeling artifacts (granular models) and their interpretation capabilities.

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. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a 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 has published numerous papers in this area. He is also an author of 16 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering.

Dr. Pedrycz is vigorously involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Int. J. of Granular Computing (Springer).  He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.

 

 

 

 

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