Course: Expert Systems

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Course title Expert Systems
Course code KIN/ES-D
Organizational form of instruction Lecture + Lesson
Level of course Doctoral
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Optional
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Lecturer(s)
  • Hájek Petr, doc. Ing. Ph.D.
Course content
Current approaches to knowledge representation - declarative schemes, semantic schemes, situational frameworks, action frames, inference in semantic networks. Methods of informed search and solution of tasks in state space. Synthesis and analysis of decision processes with uncertainty. Task solving and activity planning using propositional fuzzy logic and predicate fuzzy 1st order logic. Approaches to the acquisition of expert knowledge. Resolution principle in propositional fuzzy logic. Creation of diagnostic expert systems using AND / OR graphs and production rules. Plausible inference, Intensive Approach, and Extensive Approach. Bayesian method, Dempster-Shafer method, multi-valued logic. Design of expert systems with uncertainty. Current approaches to management and decision-making based on uncertain systems of uncertainty. Semantic networks.

Learning activities and teaching methods
Monological explanation (lecture, presentation,briefing), Self-study (text study, reading, problematic tasks, practical tasks, experiments, research, written assignments)
  • Class attendance - 280 hours per semester
Learning outcomes
The aim of the course is to acquaint students with contemporary theoretical apparatus in the field of expert systems and methods of their design.
Students obtain knowledge in given course in accordance with requirements and course programme.
Prerequisites
unspecified

Assessment methods and criteria
Oral exam, Essay, Student's performance analysis

processing, presentation and successful defense of the project with respect to the subject and doctoral theses, exam: oral with at least 60% success rate.
Recommended literature
  • GIARRATANO J. C., RILEY G. D. Expert Systems: Principles and Programming. PWS Publishing. PWS Publishing, 1998.
  • JACKSON P. Introduction to Expert Systems. Addison-Wesley, 1999.
  • KELEMEN J., LIDAY M. Expertné systémy pre prax. SOFA, Bratislava, 1996.
  • LEVESQUE H. J., LAKEMEYER G. The Logic of Knowledge Bases. MIT Press, 2001.
  • NEGNEVITSKY M. Artificial Intelligence : A Guide to Intelligent Systems. Addison Wesley, 2nd Edition, 2004.
  • NILSSON N. J. Artificial Intelligence: A New Synthesis. Morgan Kaufmann, 1998.
  • OLEJ V., PETR P. Expertní systémy. část I.. Univerzita Pardubice, 1997.
  • RUSSEL S., NORVIG P. Artificial Intelligence. A Modern Approach. Prentice Hall, Second Edition, New Jersey, 2003.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester