|Course title||Basic of artificial inteligence|
|Lecturer||Darko Dukić, Ph.D., Associate Professor, Stojanka Dukić, PhD.|
|Course objective||The course goal is to familiarize students with methods, techniques, acchievements and appling of artificial intelligence.|
|Learning outcomes:||After successfully completed course, student will be able to:
2. Use Expert systems.
3. Model system for multicriterial suboptimal deceison through various forms of knowledge views as well as knowledge base definition.
4. Use agents and multi-agents systems.
5. Use conclusion rules in indirect logic.
6. Use of neural networks in deceison making.
|Correlation of learning outcomes, teaching methods and evaluation||
|Gained competencies||After successfully completing the course the student will be able to distinguish artificial intelligence from natural as well as to describe and apply basic ideas of artificial intelligence.|
|Content (Course curriculum)||Basic definitions and classifications of intelligence and artificial intelligence. Turing’s test. Expert systems (definition, architecture, application). Knowledge view, methods and techniques of knowledge view. Knowledge view formalism using semantic diagrams. Agents and multi-agents of intelligence systems. Fuzzy sets and properties. Operations on fuzzy sets. Fuzzy arithmetic. Fuzzy logic. Rules of conclusions in fuzzy logic. Conclusions on time relations in temporal rich domains. Neural networks.|
|Recommended reading||1. Russell S., Norvig P., Artificial Intelligence – A Modern Approach, 2nd Ed., Prentice Hall, 2003.
2. Haykin S., Neural Networks, Comprehensive Foundation, 2nd, Prentice Hall, 1999.
3. Zimmermann, H.J., Fuzzy Set Theory and Its Applications, 2nd. Ed., Kluwer Academic Publishers, 1991
|Additional reading||1. Klir G.J.,. Fogler T.A, Fuzzy Sets, Uncertanity and Information, Prentice Hall, Englewood Cliffs, New York, 1988.
2. Kaufmann A., Gupta M.M., Introduction to Fuzzy Arithmetic, Theory and Applications, Van Nostrand Reinhold, New York, 1991.
|Instructional methods||Lectures (30), Laboratory exercises (30).|
|Exam formats||Two preliminary exams during the semester or written/oral examination.|
|Quality control and successfulness follow up||Students’ survey, lecturer and expert evaluation, student’s success on exams, international supervision.|