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Lecturer(s)
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Doležel Petr, prof. Ing. Ph.D.
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Course content
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Lecture topics by week of the semester: 1. Introduction to AI (basic concepts, breakdown of AI), history of AI. 2. Basics of graph theory, transition system, scheduling. 3. Transition system, problem formulation, uninformed state space search methods (breadth-first search, depth-first search). 4. Uninformed state space search methods (depth-first search with constraints, iterative depth-first search, bidirectional search, breadth-first search by price). 5. Informed state space search methods, heuristic function creation. 6. Brief introduction to game theory, minimax algorithm and its extension to alpha-beta pruning. 7. Introduction to fuzzy set theory, basic concepts, basic operations. 8. Fuzzy relations, operations with fuzzy relations, fuzzy numbers, extension principle. 9. Language variable, fuzzy logic and approximate inference. 10. Fuzzy logic systems and their applications. 11. Stochastic optimization methods (Monte-Carlo, climbing algorithm, forbidden search, simulated annealing, ant colonies). 12. Genetic algorithm, differential evolution. 13. Hybrid evolutionary algorithms. Seminar topics by week of the semester: 1. Introduction Python. 2. Basics of graph theory, transition system, scheduling. 3. Transition system, problem formulation, uninformed state space search methods (breadth-first search, depth-first search). 4. Uninformed state space search methods (depth-first search with constraints, iterative depth-first search, bidirectional search, breadth-first search by price). 5. Informed state space search methods, heuristic function creation. 6. Brief introduction to game theory, minimax algorithm and its extension to alpha-beta pruning. 7. Introduction to fuzzy set theory, basic concepts, basic operations. 8. Fuzzy relations, operations with fuzzy relations, fuzzy numbers, extension principle. 9. Language variable, fuzzy logic and approximate inference. 10. Fuzzy logic systems and their applications. 11. Stochastic optimization methods (Monte-Carlo, climbing algorithm, forbidden search, simulated annealing, ant colonies). 12. Genetic algorithm, differential evolution. 13. Hybrid evolutionary algorithms.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Methods of individual activities
- Contact teaching
- 65 hours per semester
- Individual project
- 45 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 problém 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. The examination takes the form of an oral interview.
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Recommended literature
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LAWLESS, William, Ranjeey MUTTU, Donald; SOFGE, Ira S. MISKOWITZ a Stephen RUSSELL. Artiticial Intellignece for the Internet of Everything. London: Elsevier, 2019. ISBN 978-0-1281-7636-8.
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LUCCI, Stephen a Danny KOPEC. Artificial Intelligence in the 21st Centruy. 2nd Edition. Herndon: Mercury Learning and Information, 2016. ISBN 978-1-942270-00-3.
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Olej, Vladimír. Úvod do umělé inteligence : moderní přístupy : distanční opora. Pardubice: Univerzita Pardubice, 2010. ISBN 978-80-7395-307-2.
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RUSSELL, Stuart J., Peter NORVIG a Ernest DAVIS. Artifical intelligence: a modern approach. 3rd ed.. Upper Saddle River: Prentice Hall, 2010. ISBN 978-0-13-604259-7.
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Škrabánek, Pavel. Teorie fuzzy množin a jejich aplikace (online).. Pardubice: Univerzita Pardubice, 2014. ISBN 978-80-7395-875-6.
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Volná Eva. Umělá inteligence: rozpoznávání vzorů v dynamických datech.. Praha - BEN- technická literatura, 2014. ISBN 978-80-7300-497-2.
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