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Course detail

Expert Systems

Course unit code: FSI-VEX-K
Academic year: 2016/2017
Type of course unit: compulsory
Level of course unit: Master's (2nd cycle)
Year of study: 2
Semester: winter
Number of ECTS credits:
Learning outcomes of the course unit:
Knowledge of basic principles of working and building expert systems. Ability to select and apply a proper tool for building an expert system.
Mode of delivery:
20 % face-to-face, 80 % distance learning
Mathematical logic, set theory, probability theory, basic knowledge of artificial intelligence.
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
The course deals with the following topics: Architecture and properties of expert systems. Knowledge representation, inference mechanisms. Representing and handling uncertainty. Fuzzy logic, linguistic models, fuzzy expert systems. Tools for building expert systems. Knowledge acquisition, machine learning. Characteristics and demonstrations of selected expert systems. Examples of expert system applications.
Recommended or required reading:
Mařík, V. a kol. Umělá inteligence (1, 2). Praha, Academia 1993, 1997.
Giarratano, J., Riley, G. Expert Systems. Principles and Programming. Boston, PWS Publishing Company 1998.
Jackson, P. Introduction to Expert Systems. Harlow, Addison-Wesley 1999.
Berka, P. a kol. Expertní systémy. Skripta. Praha, VŠE 1998.
Siler, W., Buckley, J.J. Fuzzy Expert Systems and Fuzzy Reasoning. Hoboken, New Jersey, John Wiley & Sons, Inc. 2005.
Kelemen J. a kol. Tvorba expertních systémů v prostředí CLIPS. Praha, Grada 1999.
Berka, P. Dobývání znalostí z databází. Praha, Academia 2003.
Planned learning activities and teaching methods:
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes:
Course-unit credit requirements: active attendance at the seminars, creating simple expert system applications.
Examination: written test (simple problems and theoretical questions), oral exam.
Language of instruction:
Work placements:
Not applicable.
Course curriculum:
Not applicable.
The goal of the course is to make students familiar with the principles of working expert systems. They will acquire fundamentals of knowledge engineering.
Specification of controlled education, way of implementation and compensation for absences:
Attendance at the seminars is controlled. An absence can be compensated for via solving given problems.

Type of course unit:

Tuition: 17 hours, optionally
Teacher / Lecturer: RNDr. Jiří Dvořák, CSc.
Syllabus: 1. Introduction to the CLIPS system – facts, templates, rules, patterns, process of inference.
2. Functions in CLIPS, definition of user functions.
3. Characteristic features and structure of expert systems, fields of applications.
4. Rule-based expert systems.
5. Introduction to Prolog.
6. Building expert systems in Prolog.
7. Expert systems based on non-rule and hybrid knowledge representation.
8. Probabilistic approaches to handling uncertainty, Bayesian nets.
9. Handling uncertainty by means of certainty factors and Dempster-Shafer theory.
10. Fuzzy approaches to handling uncertainty.
11. Fuzzy expert systems.
12. Process of building expert system, knowledge engineering.
13. Data mining.
Controlled Self-study: 35 hours, compulsory
Teacher / Lecturer: prof. RNDr. Miloslav Druckmüller, CSc.
Syllabus: 1. Introduction to the use of CLIPS system, facts and rules.
2. Templates, solving problems in CLIPS.
3. Defining and using functions in CLIPS.
4. Building expert systems in CLIPS.
5. Introduction to the use of Prolog language.
6. Solving problems in Prolog.
7. Building expert systems in Prolog.
8. The FEL-Expert system.
9. The HUGIN system.
10. Implementation of certainty factors in CLIPS.
11. The EXSYS system.
12. The LMPS system.
13. Evaluating of semester projects.

eLearning: opened course

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