Multi-valued Logic Applications (FSI-SAL)

Academic year 2021/2022
Supervisor: prof. RNDr. Miloslav Druckmüller, CSc.  
Supervising institute: ÚM all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
The aim of the course is to provide students with information about the use of Multi-valued logic in technical applications.
Learning outcomes and competences:
Knowledge of multi-valued logic, fuzzy sets theory and its use in technical applications, including practical experience with today´s expert systems.
Prerequisites:
Mathematical logic, fuzzy set theory
Course contents:
The course is intended especially for students of mathematical engineering. It includes the theory of multi-valued logic, theory of linguistic variable and linguistic models and theory of expert systems based on these topics. Particular technical applications of these mathematical teories are included as a practice.
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 is awarded on condition of having worked out a semester work.
The exam has a written and oral part.
Controlled participation in lessons:
Atendance at seminars is controlled. An absence can be compensated for via solving additional problems.
Type of course unit:
    Lecture  13 × 2 hrs. compulsory                  
    Computer-assisted exercise  13 × 1 hrs. compulsory                  
Course curriculum:
    Lecture 1. Multi-valued logic, formulae
2. T-norms, T-conorms, generalized implications
3. Linguistic variables and linguistic models
4. Knowledge bases of expert systems
5-6. Semantic interpretations of knowledge bases
7. Inference techniques and its implementation
8. Redundance a contradictions in knowledge bases
9. LMPS system
10. Fuzzification and defuzzification problem
11. Technical applications of multi-valued logic and fuzzy sets theory
12. Expert systems
13. Overview of AI methods
    Computer-assisted exercise 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
Literature - fundamental:
4. Druckmüller, M.: Technické aplikace vícehodnotové logiky, PC- DIR , Brno 1998
Literature - recommended:
1. Neural Networks and Deep Learning. Online. Michael Nielsen, 2015. Dostupné z: http://neuralnetworksanddeeplearning.com/. [cit. 2023-10-30].
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
M2A-P full-time study M-MAI Mathematical Engineering -- GCr 4 Compulsory 2 2 W