TBA.

Dr. Dimitrios A. Karras received his Diploma and M.Sc. Degree in Electrical and Electronic Engineering from the National Technical University of Athens (NTUA), Greece in 1985 and the Ph. Degree in Electrical Engineering, from the NTUA, Greece in 1995, with honors. From 1990 and up to 2004 he collaborated as visiting professor and researcher with several universities and research institutes in Greece. Since 2004, after his election, he has been with the Sterea Hellas Institute of Technology, Automation Dept., Greece as associate professor in Digital Systems and Signal Processing, till 12/2018, as well as with the Hellenic Open University, Dept. Informatics as a visiting professor in Communication Systems (the latter since 2002 and up to 2010). Since 10/2018 is Assoc. Prof. Dr. with the EPOKA university, Computer Engineering Dept., Tirana, Albania as well as Associate Prof. in Digital Systems and Intelligent Systems, Signal Processing, in National & Kapodistrian University of Athens, Greece, School of Science, Dept. General. He has published more than 70 research refereed journal papers in various areas of intelligent and distributed/multiagent systems, pattern recognition, image/signal processing and neural networks as well as in bioinformatics and more than 185 research papers in International refereed scientific Conferences. His research interests span the fields of intelligent and distributed systems, multiagent systems, pattern recognition and computational intelligence, image and signal processing and systems, biomedical systems, communications and networking as well as security. He has served as program committee member as well as program chair and general chair in several international workshops and conferences in the fields of signal, image, communication and automation systems. He is, also, former editor in chief (2008-2016) of the International Journal in Signal and Imaging Systems Engineering (IJSISE), academic editor in the TWSJ, ISRN Communications and the Applied Mathematics Hindawi journals as well as associate editor in various scientific journals, including CAAI, IET. He has been cited in more than 2144 research papers, his H/G-indices are 19/47 (Google Scholar) and his Erdos number is 5. His RG score is 30.96.

The invited speech is entitled "On uncertainty management in intelligent systems computations". With the rapid growth in the quantity and complexity of scientific knowledge available, the problems associated with dealing with this knowledge are well-recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by binding background knowledge, the querying and combining tasks can be carried out more judiciously. In this speech, we review some of the significant proposals for formalisms for representing and reasoning with uncertainty taking special attention into analyzing interval analysis representation of intelligent systems computations, which has been recently employed by the speaker in several algorithms.

Uncertainty can be classified into four classes: epistemic, linguistic, ambiguity and variability. Epistemic uncertainty is uncertainty due to lack of knowledge. It is also referred to as state of subjective uncertainty, reducible uncertainty, Type B or knowledge uncertainty means uncertainty can be reduced through more relevant data and includes systematic error, subjective uncertainty and measurement uncertainty. The epistemic uncertainty can be represented in many ways including probabilistic theory, fuzzy set, possibility theory, etc. Linguistic uncertainty produced by statements in natural language. Uncertainty analysis is a process that measures, recognizes, identifies and minimizes all types of uncertainty in risk estimates and separates this uncertainty among the risk factors that contributes to relevant risk estimates. The uncertainty analysis includes many statistical problems such as: uncertainty factor, decision making with uncertain information, estimation of uncertainty in complex models of risk, structural uncertainty and model specification and monitoring methods to reduce uncertainty. Quantitative approaches to measure uncertainty vary with complexity of the problem and the associated methods to reduce risk.

This plenary lecture will, therefore, deal with all major tools for handling uncertainty in knowledge representation, including probability theory, fuzzy sets, possibility theory, statistical estimation but special attention will be given to interval analysis as a new and very promising tool for dealing with uncertainty, emphasizing and analyzing its advantages and shortcomings.