Course detail

Artificial Intelligence Algorithms

FSI-VAI-KAcad. year: 2024/2025

The course introduces basic approaches to artificial intelligence algorithms and classical methods used in the field. Main emphasis is given to automated formulas proves, knowledge representation and problem solving. Practical use of the methods is demonstrated on solving simple engineering problems.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

prof. Ing. Radomil Matoušek, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

Entry knowledge

Knowledge of algorithmization, programming and the basics of mathematical logic and probability theory are assumed.

Rules for evaluation and completion of the course

Course-unit credit requirements: Creation of functional software projects using some of the discussed AI methods and working out a presentation of some undiscussed AI method. Student can obtain 100 marks, 40 marks during seminars (30 for projects and 10 for the presentation; he needs at least 20), 60 marks during exam (he needs at least 30).
The attendance at lectures is recommended, at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a teacher.

Aims

Knowledge of the basic means of artificial intelligence and the possibilities of their use in solving engineering tasks.
Understanding of basic methods of artificial intelligence and ability of their implementation.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Edward A. Bender: Mathematical Methods in Artificial Intelligence
Kim W.Tracy, Peter Bouthoorn: Object-oriented Artificial Intelligence Using C++

Recommended reading

F.Zbořil a kol.: Umělá inteligence (skriptum VUT)

Classification of course in study plans

  • Programme N-AIŘ-K Master's 1 year of study, winter semester, compulsory

Type of course unit

 

Guided consultation in combined form of studies

17 hod., compulsory

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.

Syllabus

1. Introduction to artificial intelligence.
2. State space, uninformed search.
3. Informed search in state space.
4. Problem solving by decomposition into sub-problems, AND/OR search methods.
5. Game playing methods.
6. Constraint satisfaction problems.
7. Predicate logic and resolution method.
8. Horn logic and logic programming.
9. Representation, use and learning of knowledge.
10. Representation and processing of uncertainty.
11. Bayesian and decision networks.
12. Non-traditional logics.
13. Markov decision processes.

Guided consultation

35 hod., optionally

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.

Syllabus

1. Introductory motivational examples.
2. Uninformed methods of state space search.
3. Informed methods of state space search.
4. A* algorithm and its modifications.
5. Methods of AND/OR graph search.
6. Game playing methods.
7. Constraint satisfaction problems.
8. Predicate logic and resolution method.
9. Logic programming and Prolog.
10. Solving AI problems in Prolog.
11. Learning symbolic knowledge.
12. Bayesian networks.
13. Probabilistic and fuzzy logic programming.