Course detail

Technical Applications of Artificial Intelligence Methods

FSI-RUICompulsory-optionalMaster's (2nd cycle)Acad. year: 2016/2017Summer semester1. year of study5  credits

The course is intended for students of mathematical engineering and deals with the multi-valued logic theory, theory of linguistic varialble, linguistic models and theory of expert systems based on these topics. Also dealt with are the technical applications of multi-valued logic and expert systems in technical branches.

Learning outcomes of the course unit

Knowledge of multi-valued logic, fuzzy sets theory and its use in technical applications, including practical experience with today´s expert systems.

Mode of delivery

90 % face-to-face, 10 % distance learning

Prerequisites

Basic knowledge of mathematical logic, set theory and mathematical analysis

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Klir, J. Yuan, B.: Fuzzy sets and fuzzy logic, George J. Klir and Bo Yuan, Prentice Hall, NJ 1995
Druckmüller, M.: Technické aplikace vícehodnotové logiky, PC- DIR , Brno 1998
Druckmüller, M.: Technické aplikace vícehodnotové logiky, PC- DIR , Brno 1998

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 is awarded on condition of having worked out a semester work.
The exam has a written and oral part.

Language of instruction

Czech

Work placements

Not applicable.

Aims

The aim of the course is to provide students with information about the usage of Multi-valued logic in technical applications.

Specification of controlled education, way of implementation and compensation for absences

Atendance at seminars is controlled. An absence can be compensated for via solving additional problems.

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Multi-valued logic, formulae
2. T-norms, T-conorms, generalized implications
3. Linguistic variables and linguistic models
4. Knowledge bases of expert systems
5. Semantic interpretations of knowledge bases
6. Inference techniques and its implementation
7. Redundance a contradictions in knowledge bases
8. LMPS system
9. LMPS system - applications
10. Fuzzification and defuzzification problem
11. Technical applications of multi-valued logic and fuzzy sets theory
13. Expert systems
13. Overview of AI methods

seminars in computer labs

26 hours, compulsory

Teacher / Lecturer

Syllabus

1. Multi-valued logic, formulae
2. Lukasziewicz logic
3-4. Linguistic variables and linguistic models
5. Semester work specification
6. LMPS system - linguistic variables
7. LMPS system - statements
8. LMPS system - question and reply interpretation
9. LMPS system - debugger and redundance detection
10. LMPS system - contradictions detection and removing
11-12. Semester work consultation
13. Delivery of semester work