Expert Systems (FSI-VEX-K)

Academic year 2017/2018
Supervisor: prof. RNDr. Miloslav Druckmüller, CSc.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
The goal of the course is to make students familiar with the principles of working expert systems. They will acquire fundamentals of knowledge engineering.
Learning outcomes and competences:
Knowledge of basic principles of working and building expert systems. Ability to select and apply a proper tool for building an expert system.
Mathematical logic, set theory, probability theory, basic knowledge of artificial intelligence.
Course contents:
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.
Teaching methods and criteria:
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.
Controlled participation in lessons:
Attendance at the seminars is controlled. An absence can be compensated for via solving given problems.
Type of course unit:
    Tuition  1 × 17 hrs.
    Controlled Self-study  1 × 35 hrs.
Course curriculum:
    Tuition 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 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.
Literature - fundamental:
1. Giarratano, J., Riley, G. Expert Systems. Principles and Programming. Boston, PWS Publishing Company 1998.
2. Jackson, P. Introduction to Expert Systems. Harlow, Addison-Wesley 1999.
3. Siler, W., Buckley, J.J. Fuzzy Expert Systems and Fuzzy Reasoning. Hoboken, New Jersey, John Wiley & Sons, Inc. 2005.
Literature - recommended:
1. Mařík, V. a kol. Umělá inteligence (1, 2). Praha, Academia 1993, 1997.
2. Berka, P. a kol. Expertní systémy. Skripta. Praha, VŠE 1998.
3. Kelemen J. a kol. Tvorba expertních systémů v prostředí CLIPS. Praha, Grada 1999.
4. Berka, P. Dobývání znalostí z databází. Praha, Academia 2003.
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2I-K combined study M-AIŘ Applied Computer Science and Control -- Ac,Ex 5 Compulsory 2 2 W
M2I-K combined study M-AIŘ Applied Computer Science and Control P linked to branch B-AIR Ac,Ex 5 Compulsory 2 2 W