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Expert Systems

COURSE: Expert Systems

Code: ФЕИТ01003

ECTS points: 6 ECTS

Number of classes per week: 3+0+0+3

Lecturer: prof. Tatjana Kolemishevska-Gugulovska, PhD

Subject of the course content: An expert system is a computer system that emulates the decision-making ability and behavior of a human or organization that has expert knowledge and experience in a particular field. Expert systems are designed to solve complex problems by reasoning about knowledge. They typically contain a knowledge base, containing accumulated experience, and a set of rules for applying the knowledge base to each particular situation. It is divided into two parts, one fixed, independent of the expert system: the inference engine, and one variable: the knowledge base. To run an expert system, the engine reasons about the knowledge base like a human.

Topics that will be covered in this course are as follows: 1. Expert systems introduction; 2. Knowledge base representation; 3. Rule based systems; 4. Decision methods; 5. Reasoning in terms of uncertainties; 6. Imprecise reasoning; 7. Expert system design; 8. Applications of expert systems.

Literature:

  1. Peter Jackson, “Introduction To Expert Systems”, Addison-Wesley; 3 edition, 1998.
  2. Gonzalez and Dankel, “The Engineering of Knowledge-based Systems - Theory and Practice”, Prentice Hall Inc., 1993.
  3. Joseph C. Giarratano, Gary Riley, “Expert Systems: Principles and Programming”, Thomson Course Technology, 2005.
  4. Cornelius T. Leondes (Editor), “Knowledge-Based Systems, Four-Volume Set: Techniques and Applications”, Academic Press; 1 edition, 2000.
  5. Nikola K. Kasabov, “Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering”, The MIT Press, 1996.