Tutorial 1Instructor: Vladik Kreinovich, University of Texas at El Paso, Texas, USA
Time: Monday 4 May 2020
Instructor's biography: Vladik Kreinovich is Professor of Computer Science at the University of Texas at El Paso. His main interests are the representation and processing of uncertainty, especially interval computations and intelligent control. He has published eight books, 24 edited books, and more than 1,500 papers. Vladik is Vice President of the International Fuzzy Systems Association (IFSA), Vice President of the European Society for Fuzzy Logic and Technology (EUSFLAT), Fellow of International Fuzzy Systems Association (IFSA), Fellow of Mexican Society for Artificial Intelligence (SMIA), Fellow of the Russian Association for Fuzzy Systems and Soft Computing. He is Treasurer of IEEE Systems, Man, and Cybernetics Society.
Tutorial 2: Evaluating Uncertainty Techniques for Situation Awareness Information Fusion SystemsInstructor:
Prof. Paulo C. G. da Costa, Department of Systems Engineering and Operations Research, George Mason University, VA, USA
Time: Monday 4 May 2020
Abstract: One of the main goals of information fusion is uncertainty reduction, which is highly dependent on the representation chosen. Uncertainty representation differs across the various levels of Information Fusion (as defined by the JDL/DFIG models). High-level information fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgment that an HLIF framework must support automated knowledge representation and reasoning with uncertainty, there is no consensus on the requirements an HLIF framework must meet, on the most appropriate technologies to satisfy these requirements, and on how to evaluate how well they are being met. A clearly defined, scientifically rigorous evaluation framework and metrics are needed to help information fusion researchers assess the suitability of various approaches and tools to their applications. In this tutorial we will explore the Uncertainty Representation and Reasoning Evaluation Framework (URREF), which has been under active development by the Uncertainty Techniques for Uncertainty Representation working group of the International Society of Information Fusion (ISIF ETURWG). We will cover use cases involving Situation Awareness challenges for uncertainty representation and explore the use of URREF to address these challenges.
Instructor's biography: Paulo Cesar G. Costa is Associate Professor at the Department of Systems Engineering and Operations Research at George Mason University. His research focus is on integrating semantic technologies and uncertainty management with applications in multidisciplinary areas. He is a pioneer in the field of probabilistic ontologies and the creator of PR-OWL, a probabilistic ontology language for the Semantic Web, and a key contributor to UnBBayes-MEBN, a Java-based, open source implementation of the PROWL language. Dr. Costa's expertise derives from both his prior work as a practitioner and then researcher in the areas of transportation safety and security, operations research, information fusion, and cybersecurity; which he currently applies in research grants from DARPA, AFRL, U.S. DoT, and U.S. DoE. He is an IEEE Senior Member, member of the International Council of Systems Engineering (INCOSE), member of the Institute for Operations Research and the Management Sciences (INFORMS), and currently serves as the President of the International Society of Information Fusion (isif.org).