報告人： Witold Pedrycz，加拿大計算智能研究委員會主席，艾伯塔大學電氣與計算機工程系教授，IEEE Fellow。
報告時間：2020年12月16日 (星期三) 上午10:00 - 12:00
Meeting ID: 281 001 9605
報告摘要：In data analytics, system modeling, and decision-making models, the aspectsof interpretability and explainability are of paramount relevance, just to mention only explainable Artificial Intelligence (XAI). In this presentation, we advocate that there are two factors that immensely contribute to the realization of the above important features, namely, a suitable level of abstraction in describing the problem and a logic fabric ofthe resultant construct. It is demonstrated that their conceptualization and the following realization can be conveniently carried out with the use of information granules (for example, fuzzy sets, sets, rough sets, and alike).
報告人簡介：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 MeritoriousService 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 papers in these areas. He is also an author of 21 research monographs and edited volumes covering various aspects of Computational Intelligence, data mining, and Software Engineering.
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