Witold Pedrycz
IEEE Fellow, 1998
Professor and Canada Research Chair (CRC) in Computational Intelligence,
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.
Abstract:
In modeling complex systems, one strives to take full advantage of available numeric data and qualitative (symbolic) evidence. We anticipate that the resulting constructs (models) should meet a number of requirements. In particular, we require that they exhibit high accuracy, are interpretable and explainable as well as come with some level of stability. The tendency of satisfying (at least to some degree) all requirements outlined above is well visible in some general pursuits encountered under the banner of Artificial Intelligence (AI) and its recent developments of explainable AI (XAI).
We advocate that in addressing these timely challenges, information granules and Granular Computing play a significant role by facilitating a smooth and continuous linkage between these two views and striking a sound balance between qualitative and quantitative facets of modeling. First, it is demonstrated that information granularity is of paramount relevance in building associations between real-world data and symbols commonly encountered in AI processing. Second, we stress that a suitable level of abstraction (specificity or information granularity) becomes essential to support user-oriented framework of designing and functioning ensuing models. In both cases, central to all pursuits is a process of formation of information granules followed by their prudent characterization. We discuss a comprehensive approach to the development of information granules completed by the principle of justifiable granularity. Here various construction scenarios are discussed including those engaging conditioning and collaborative mechanisms incorporated in the design of information granules. The mechanisms of assessing the quality of granules are presented. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data delivering a required level of linguistic stability. We elaborate on the generative and discriminative aspects of information granules supporting their further usage in the formation of granular models.
In the sequel, these features are exploited in the construction of models of the required interpretability and explainability faculties as well as being endowed with summarization capabilities. We also show how such models can be formed at various levels of abstraction by engaging information granules of higher type and higher order.
Biography:
Witold Pedrycz (IEEE Fellow, 1998) 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. 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, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.
His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data science, pattern recognition, data science, knowledge-based neural networks, and control engineering. He has published numerous papers in these areas; the current h-index is 110 (Google Scholar) and 82 on the list top-h scientists for computer science and electronics http://www.guide2research.com/scientists/. He is also an author of 21 research monographs and edited volumes 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 Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer). He serves on an Advisory Board of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of international journals.