Expert Systems

Course Description

Basics of symbolic, connectivist and combined approach to artificial intelligence. Knowledge acquisition from experts. Fundamentals of automated reasoning and deductive logical systems. Application of automated reasoning in computer systems design and problem solving. Rule-based expert systems augmented with rule weighting, certainty factors, and fuzzy logic. Applications in designing technical systems, diagnostics, and process control. Probabilistic reasoning based on Bayesian belief networks. Inference methods in Bayesian networks. Parameters and structure estimation for Bayesian networks. Applications of Bayesian networks in diagnostics and prediction. Ontologies and ontological languages for semantic web. Recommender systems. Project work involves development of an expert system for a particular problem with application of prevalent expert system shells (e.g. Prover9/Mace4, CLIPS, FuzzyCLIPS, Matlab, HuginLite, Protégé).

Learning Outcomes

  1. identify main constituents of expert system
  2. distinguish class of problems suitable for solving with expert systems
  3. develop expert system suitable for solving particular problem
  4. analyze advantages and disadvantages of various types of expert systems
  5. choose a type of expert system suitable for solving particular problems
  6. collect relevant knowledge from experts
  7. arrange collected knowledge from experts so that it forms expert system knowledge base

Forms of Teaching


Live or online

Independent assignments


Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 0 % 40 % 0 % 40 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Oral 30 %
Exam: Written 0 % 30 %
Exam: Oral 30 %

Week by Week Schedule

  1. Course administration. Introduction to expert systems.
  2. Data acquisition and representation methods for expert systems
  3. Logic-based expert systems
  4. Rule-based expert systems
  5. Fuzzy expert systems
  6. Probabilistic expert systems
  7. Ontology-based expert systems
  8. Midterm exam
  9. Recommender systems. Project work
  10. Recommender systems. Project work
  11. Expert system tools. Project work
  12. Expert system tools. Project work
  13. Expert system tools. Project work
  14. Project delivery
  15. Final exam - project presentations

Study Programmes

University graduate
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(.), Peter Jackson (1999.), Introduction to expert systems, 3rd Ed., Addison Wesley,
(.), Daphne Koller, Nir Friedman (2009.), Probabilistic Graphical Models: Principles and Techniques, 1st Ed., The MIT Press,
(.), Jerry M. Mendel (2017.), Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd Ed., Springer,
(.), Charu C. Aggarwal (2016.), Recommender Systems: The Textbook, 1st Ed., Springer,
(.), Dean Allemang, James Hendler (2011.), Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, 2nd Ed., Morgan Kaufmann,

For students


ID 222573
  Summer semester
L1 English Level
L1 e-Learning
30 Lectures

Grading System

90 Excellent
75 Very Good
60 Good
50 Acceptable