UIMOIBP043 Artificial Intelligence

Faculty of Philosophy and Science in Opava
Summer 2021
Extent and Intensity
2/0/0. 4 credit(s). Type of Completion: zk (examination).
doc. Ing. Petr Sosík, Dr. (lecturer)
Guaranteed by
doc. Ing. Petr Sosík, Dr.
Institute of Computer Science - Faculty of Philosophy and Science in Opava
Tue 11:25–13:00 B2
Prerequisites (in Czech)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course provides a basic overview of the goals, methods, and techniques of contemporary artificial intelligence (AI). The explanation is centered at the view of an intelligent agent that perceives and acts in a certain environment. Due to the restricted scope of the course, we focus on rational behaviour of the agent, i.e., how to reach its goals most efficiently. Both agents' capabilities and properties of the environment must be considered. An introduction to rational agents' construction is followed by techniques of the state space search, including a multi-agent environment and basic game strategies. The course is concluded by the basics of machine learning, including artificial neural networks.
Learning outcomes
Students will be able to:
- identify and summarize the basic features of contemporary artificial intelligence.
- Apply selected techniques enabling intelligent machine behavior.
- Describe and apply basic machine learning methods.
  • 1. Introduction, history, artificial intelligence evaluation, and applications.
  • 2. Intelligent agents: reactivity and deliberation, the subsumption architecture, model-based agents.
  • 3. State-space search, informed and uninformed strategies, heuristic functions. 4. Local and online search.
  • 5. Constraints satisfaction problems and search.
  • 6. State-space in games, strategies and adversarial search.
  • 7. Knowledge representation for intelligent agents.
  • 8. Basics of machine learning, decision trees, regression.
  • 9. Artificial neural networks.
  • 10. Advanced AI topics - an overview.
    required literature
  • MAŘÍK, V. a kol. and MAŘÍK, V. a kol. Umělá inteligence (1-6). Praha,: Academia, 2013. info
  • RUSSEL, Stuart J, Peter NORVIG and Ernest DAVIS. Artificial intelligence: a modern approach. 3rd ed. Upper Saddle River: Prentice Hall, 2010. ISBN 978-0-13-604259-4. info
    recommended literature
  • KULKARNI, Parag and Prachi JOSHI. Artificial Intelligence: Building Intelligent Systems. Delhi: PHI PrivateLearning, 2015. ISBN 978-81-203-5046-5. info
  • HARRIS, Michael C. Artificial intelligence. New York: Marshall Cavendish Benchmark, 2011. ISBN 978-1-60870-076-9. info
  • Ivan Bratko. Prolog Programming for Artificial Intelligence. 2011. ISBN 978-0321417466. info
  • KELEMEN, J. a kol. Základy umelej inteligencie. Bratislava, ALFA, 1992. info
Teaching methods
Interactive lecture
Lecture with a video analysis
Assessment methods
Exam: Develop answers to a set of theoretical questions. Elaborate a semester work on artificial intelligence programming techniques.
Language of instruction
Further Comments
Study Materials

  • Enrolment Statistics (recent)
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