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Lecturer(s)
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Olej Vladimír, prof. Ing. CSc.
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Course content
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The artificial intelligence programming language PROLOG. Knowledge and expert systems. Knowledge representation. Framework, procedural and declarative schemes of knowledge representation. Synthesis and analysis of decision-support processes with uncertainty. Fuzzy sets, statement fuzzy logic. The artificial intelligence programming language fuzzy PROLOG. Resolution principle in propositional logic and propositional fuzzy logic. Plausible inference, intensional and extensional approach. Bayes method, Dempster-Shafer method, multi-value logic. Basic knowledge and expert systems design. Knowledge and expert systems programming. Example of neural network as a simple knowledge system in management and decision-support.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Laboratory work
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Learning outcomes
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The aim of the course is to provide basic knowledge in the area of artificial and computational intelligence and the possibilities for its use in various areas of social life, especially the economics, social and environmental fields.
The students should be able to design basic knowledge systems and expert systems in general (binary or with uncertainty) in the artificial intelligence programming language PROLOG, as well as be able to use the artificial intelligence methods in decision-making and management.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral examination, Written examination, Didactic test, Discussion, Class observation
Completion and successful defence of two projects with a success rate of at least 60 %, written tests throughout the semester. Assessment result consists of 50 % project result and 50 % written test result. The examination is written.
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Recommended literature
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NILSSON, N. J. Artificial Intelligence: A New Synthesis. Morgan Kaufmann, 1998, ISBN 1558604677. Prentice Hall, Second Edition, New Jersey, 2003. ISBN 80 7194-670.
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