Students learn about the basic principles and techniques of "classical" artificial intelligence and soft computing.

Identification, structure and techniques of solving problems which require intelligence.

Concepts, aims, approaches, environment and areas of AI application. Logical programming: propositional and first order logic; Prolog programming language. Search: blind and heuristic search, genetic algorithms. Problem solving in uncertainty conditions: probabilistic approach, fuzzy approach. Fundamentals of machine learning, types of algorithms, approaches, artificial neural networks. Knowledge based systems. Intelligent software agents: definition, types, architecture, technologies. Applications of AI.

Lectures, Computer practice. Consultations. Practical part of the course is examined in the computer laboratory where students solve obligatory tasks. Students can also do nonobligatory laboratory tasks. The task are marked. Part of the course which forms a logical whole can be taken in the form of partial exam – colloquium (2-4). Partial exam is a part of the examination. A student can take the next partial examination if he/she has achieved at least 30% of the point at the previous one. Partial examinations are taken in written form. The final examination is oral. Course grade is formed on the basis of lecture attendance, marks on the obligatory and nonobligatory tasks, success at partial examinations and final examination.