Course detail

Expert Systems

FSI-VEXAcad. year: 2018/2019

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.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

prof. RNDr. Miloslav Druckmüller, CSc.

Department

Institute of Mathematics (ÚM)

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.

Prerequisites

Mathematical logic, set theory, probability theory, basic knowledge of artificial intelligence.

Co-requisites

Not applicable.

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.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

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 required. An absence can be compensated for via solving given problems.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Bratko, I. Prolog Programming for Artificial Intelligence. Pearson Education 2011. (EN)
Giarratano, J., Riley, G. Expert Systems. Principles and Programming. Boston, PWS Publishing Company 1998. (EN)
Jackson, P. Introduction to Expert Systems. Harlow, Addison-Wesley 1999. (EN)
Mitchell, T. M. Machine Learning. Singapore, McGraw-Hill 1997. (EN)
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Harlow, Addison-Wesley 2005. (EN)
Siler, W., Buckley, J.J. Fuzzy Expert Systems and Fuzzy Reasoning. Hoboken, New Jersey, John Wiley & Sons, Inc. 2005. (EN)

Recommended reading

Berka, P. a kol. Expertní systémy. Skripta. Praha, VŠE 1998. (CS)
Berka, P. Dobývání znalostí z databází. Praha, Academia 2003. (CS)
Jackson, P. Introduction to Expert Systems. Harlow, Addison-Wesley 1999. (EN)
Kelemen J. a kol. Tvorba expertních systémů v prostředí CLIPS. Praha, Grada 1999. (CS)
Mařík, V. a kol. Umělá inteligence (1, 2, 4). Praha, Academia 1993, 1997, 2003. (CS)
Merrit, D. Building Expert Systems in Prolog. Berlin, Springer-Verlag 1989. http://www.amzi.com/ExpertSystemsInProlog/index.htm (EN)
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Harlow, Addison-Wesley 2005. (EN)
Polák, J. Prolog. Praha, Grada 1992. (CS)

Classification of course in study plans

  • Programme M2I-P Master's

    branch M-AIŘ , 2 year of study, winter semester, compulsory
    branch M-AIŘ , 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., 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.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

RNDr. Jiří Dvořák, 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. Pseudo-bayesian systems.
9. Bayesian networks.
10. Implementation of certainty factors in CLIPS.
11. EXSYS and FLEX systems.
12. LMPS system.
13. Evaluating of semester projects.