Abstract: Knowledge-based systems (KBS) are computer systems using methods and techniques of artificial intelligence. The main components of the systems are the knowledge base and the inference procedure. Such systems are capable of supporting human decisions in many fields of activities, as: control, prediction, diagnosis and many others.
In this work we present the general methods of creating the probabilistic-fuzzy knowledge base and the inference procedure. A structure of the reason-result fuzzy model is predefined according to expert’s experiences, at the beginning of the task. Sets of numerical data, collected in real systems are used to compute empirical probability distributions of linguistic variables of the knowledge representation. The calculated probabilities of fuzzy events have been included into inference procedures.
Models and inference procedures for exemplary tasks are presented.

 

Brief Biography of the Speaker:
Anna Walaszek-Babiszewska, at present, is a professor at the Opole University of Technology, Department of Control and Computer Engineering. She has obtained a MSc degree in Control Engineering from the Wroclaw University of Technology and a PhD as well as a DSc (Habilitation) degrees from the Silesian University of Technology in Gliwice, Poland.
Her research interests include: stochastic and fuzzy modeling, fuzzy systems identification, data analysis, and applications in technological and managerial situations.
She has supervised 3 completed PhDs and reviewed 5 PhDs in technical and economic sciences. She has published 2 monographic books on stochastic and fuzzy modeling and over 80 scientific papers.
She was a member of the Editorial Boards of Management (2000-2005) and Lecture Notes in Control and Computer Science (2003) of the Zielona Gora University Press. She is a member of the Section of Cybernetics in Mining, Mining Committee of the Polish Academy of Sciences (since 1999).

 

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