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Course detail

Technical Applications of Artificial Intelligence Methods

Course unit code: FSI-RUI
Academic year: 2016/2017
Type of course unit: compulsory-optional
Level of course unit: Master's (2nd cycle)
Year of study: 1
Semester: summer
Number of ECTS credits:
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.
Course contents (annotation):
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.
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.
Course curriculum:
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: prof. RNDr. Miloslav Druckmüller, CSc.
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: prof. RNDr. Miloslav Druckmüller, CSc.
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

The study programmes with the given course