Course detail

Expert Systems

FSI-VEXAcad. year: 2011/2012

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. Hybrid 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

5

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

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Course-unit credit requirements: active attendance at the seminars, creating a simple application of an expert system.
Examination: written test (simple problem 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 controlled. An absence can be compensated for via solving given problems.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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)
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)

Classification of course in study plans

  • Programme N2301-2 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. Characteristic features and structure of expert systems, fields of applications.
2. Introduction to the CLIPS system – facts, templates, rules, patterns, process of inference.
3. Functions in CLIPS, definition of user functions.
4. Rule-based expert systems, inference mechanisms.
5. Semantic nets, frames and objects, blackboard architectures.
6. Objects in CLIPS.
7. Probabilistic approaches to handling uncertainty, Bayesian nets.
8. Handling uncertainty by means of certainty factors and Dempster-Shafer theory.
9. Fuzzy approaches to handling uncertainty.
10. Fuzzy expert systems.
11. Hybrid expert systems.
12. Process of building expert system, knowledge engineering.
13. Data mining.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Petr Krček, Ph.D.

Syllabus

1. Introduction to the use of CLIPS system, facts and rules.
2. Solving simple problems in CLIPS.
3. Solving problems in CLIPS using templates.
4. Defining and using functions in CLIPS.
5. Using objects in CLIPS.
6. Examples of expert systems in CLIPS.
7. Building an expert system in CLIPS.
8. Implementation of handling uncertainty in CLIPS.
9. The EXSYS system, examples of applications.
10. The HUGIN system, examples of applications.
11. Introduction to the LMPS system.
12. Introduction to the Fuzzy CLIPS system.
13. Evaluating of semester projects.