1. Course Artificial Intelligence (AI), the definition, classification.
2. Fuzzy logic (FL), fuzzification, defuzzyfication, inference rules, fuzzy controllers.
3. Evolution algorithms (EA). Method of differential evolution (DE), self-organizing migration algorithm (SOMA). Genetic algorithms (GA), inheritance, crossover, mutation, selection, fitness function.
4. Artificial Neural Networks (ANN), division, operating principles, characteristics, processing of uncertain information in a neural network, learning in neural networks.
5. Multi-Agent Systems (MAS), distributed artificial intelligence, the concept of agent, distribution agent, adaptability, communication.
6. Expert systems, principles, knowledge base, knowledge-based systems.
7. Software tools used in the implementation of the AI.
8. Solving of problems of interpretation, diagnosis, prediction.
9. Task planning and recognition.
10. Control and optimization solving with using AI.
2. Fuzzy logic (FL), fuzzification, defuzzyfication, inference rules, fuzzy controllers.
3. Evolution algorithms (EA). Method of differential evolution (DE), self-organizing migration algorithm (SOMA). Genetic algorithms (GA), inheritance, crossover, mutation, selection, fitness function.
4. Artificial Neural Networks (ANN), division, operating principles, characteristics, processing of uncertain information in a neural network, learning in neural networks.
5. Multi-Agent Systems (MAS), distributed artificial intelligence, the concept of agent, distribution agent, adaptability, communication.
6. Expert systems, principles, knowledge base, knowledge-based systems.
7. Software tools used in the implementation of the AI.
8. Solving of problems of interpretation, diagnosis, prediction.
9. Task planning and recognition.
10. Control and optimization solving with using AI.