Uncertainty Based Reasoning Systems

Study program: Mechanical engineering
Type and level of studies:  Doctoral studies
Course unit: pdfUncertainty Based Reasoning Systems
Course code: DS21010
Teacher in charge:  pdfProf. Dr. Mirko Djapic
Language of instruction: English
Semester:  Spring
Course unit objective:
To introduce students with the mathematical tools (beliefs functions) and the way of their selection in the process of modeling and reasoning on the basis of uncertainty in the fields of engineering and management
Learning outcomes of the course unit
Students should acquire knowledge and skills that will enable them to select and apply of appropriate models, the beliefs functions by which they will modeling the uncertainty of the problems in the field of engineering and management
Course unit contents
Theoretical classes
The concept, definition and division of uncertainty in engineering, Modeling aleatory and epistemic uncertainty, Dempster-Shafer's theory of belief functions, Entropy of the belief functions, Graphical models (frames) for presentation uncertainty knowledge, Processing uncertainty knowledge and reasoning on the basis of uncertainty - Expert Systems for the processing of uncertain knowledge, Valuation systems, Evidential networks, Bayesian belief networks, Application examples of the evidential networks in engineering and management.
Practical classes
A student project consists of modeling uncertainty in the selected problem by the evidence networks
  1. G. Shafer (1976): A Mathematical Theory of Evidence, Princeton University Press.
  2. P. Shenoy (1992): Valuation-Based Systems: A framework for managing uncertainty in expert systems, John Wiley & Sons, New York, 1992.
  3. B.M. Ayyub, G.J. Klir (2006): Uncertainty Modeling and Analysis in Engineering and the Sciences, Champman & Hall/CRC Taylor &Ftancis Group, Boca Raton, FL.