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Lecturer(s)
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Pozdílek Martin, Ing. Ph.D.
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Course content
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1. Introduction (Introduction to AI, basic terms, AI segmentation). 2. Graph theory basics, planning. 3. Transition system, problem formulation, uninformed state-space search (depth first search, breadth first Search). 4. Uninformed state-space search (limited depth first search, iterative depth first search, lowest cost first Search). 5. Informed state-space search, heuristic function. 6. Games theory, minimax algorithms, minimax algorithm with alfa-beta prunning, heuristic minimax. 7. Fuzzy sets, basic operations. 8. Fuzzy relations and operations with fuzzy relations, fuzzy numbers. 9. Linguistic variable, fuzzy logic. 10. Fuzzy logic systems. 11. Knowledge, knowledge representations, expert systems 12. Expert systems. 13. Repetition.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Methods of individual activities
- Individual project
- 45 hours per semester
- Contact teaching
- 65 hours per semester
- Home preparation for classes
- 26 hours per semester
- Preparation for an exam
- 14 hours per semester
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Learning outcomes
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The main aim of the course is to familiarize students with the basics and the structure of the artificial intelligence scientific discipline. In additition, students will be provided with knowledge in problem solving, games theory, planning, fuzzy logic and expert systems.
Basic orientation in the artificial inteligence problems. Ability to use optimization methods, problém solving techniques, fuzzy systems building and orientation in expert system problems.
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Prerequisites
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There is expected fundamental knowledge of programming and graph theory.
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Assessment methods and criteria
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Oral examination, Home assignment evaluation
Solving of the final complex project comprising of different tasks from individual parts of the subject.
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Recommended literature
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NEGNEVITSKY M. Artificial Intelligence : A Guide to Intelligent Systems (2nd Edition).. Addison Wesley, 2004. ISBN 0321204662.
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Russell, Stuart J. Artificial intelligence : a modern approach. Harlow: Pearson Education, 2014. ISBN 978-1-292-02420-2.
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ŠKRABÁNEK, P. Základy umělé inteligence (elektronické učební texty. 2010.
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