Course: Expert Systems

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Course title Expert Systems
Course code FES/HESY
Organizational form of instruction Lecture + Seminar
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Hájek Petr, prof. Ing. Ph.D.
Course content
Contemporary approaches to knowledge representation - Declarative schemas, semantic schemas, situational frames, action frames, inference in semantic networks. Methods of informed search and problem-solving in state space. Synthesis and analysis of decision-making processes under uncertainty. Problem-solving and activity planning using propositional fuzzy logic and first-order predicate fuzzy logic. Approaches to expert knowledge acquisition. Resolution principle in propositional fuzzy logic. Development of diagnostic expert systems using AND/OR graphs and production rules. Plausible inference, intensional approach, and extensional approach. Bayesian method, Dempster-Shafer method, and multi-valued logic. Design of expert systems under uncertainty. Contemporary approaches to control and decision-making based on expert systems with uncertainty.

Learning activities and teaching methods
Methods of individual activities
Learning outcomes
The aim of the course is to acquaint students with contemporary theoretical frameworks in the field of expert systems and methods for their design.
The students should be able to design knowledge base for binary expert systems and expert systems with uncertainty.
Prerequisites
unspecified

Assessment methods and criteria
Discussion

Completion and successful defense of a project on a selected topic, focusing on a written paper for the state doctoral exam and the doctoral dissertation. Examination: Oral, with a minimum pass rate of 60%.
Recommended literature
  • AKERKAR, R. A., SAJJA, P. S. Knowledge-based systems. Boston: Jones and Bartlett Publishers, 2010.
  • HECKERMAN, D., HORVITZ, E., NATHWANI, B. N. Toward normative expert systems - Part I. Methods of Information in Medicine, Vol. 31, pp 90-105. 1992.
  • KOLLER, D., FRIEDMAN, N. Probabilistic graphical models: Principles and techniques. Cambridge: The MIT Press, 2009.
  • LIAO, S. H. Expert system methodologies and applications - A decade review from 1995 to 2004. Expert Systems with Applications, Vol. 28, pp 93-103. 2005.
  • NEGNEVITSKY M. Artificial Intelligence : A Guide to Intelligent Systems (2nd Edition).. Addison Wesley, 2004. ISBN 0321204662.
  • RUSSEL S., NORVIG P. Artificial Intelligence. A Modern Approach.. Prentice Hall, Second Edition, New Jersey, 2003.
  • WAGNER, W. P. Trends in expert system development: A longitudinal content analysis of over thirty years of expert system case studies. Expert Systems with Applications, Vol. 76, pp 85-96. 2017.


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