Course detail

Artificial Intelligence Algorithms

FSI-VAIAcad. year: 2019/2020

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.

Learning outcomes of the course unit

Understanding of basic methods of artificial intelligence and ability of their implementation.


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


Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Harlow, Addison-Wesley 2005. (EN)
Edward A. Bender: Mathematical Methods in Artificial Intelligence. IEEE Computer Society Press 1996. (EN)
Zbořil, F. a kol.: Umělá inteligence (skriptum VUT). (CS)
Russel, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall 2009. (EN)
Mařík, V. a kol.: Umělá inteligence. Praha, Academia. (CS)
Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley 2008. (EN)
Poole, D.L., Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press 2017. (EN)
Mitchell, T. M. Machine Learning. Singapore, McGraw-Hill 1997. (EN)

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 requirements: passing partial tests and submitting a functional software project which uses implementation of selected AI method. Student can obtain 100 marks, 40 marks during seminars (20 for tests and 20 for project; he needs at least 20), 60 marks during exam (he needs at least 30).

Language of instruction


Work placements

Not applicable.


The course objective is to make students familiar with basic resources of artificial intelligence, potential and adequacy of their use in engineering problems solving.

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

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 tutor.

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MAI , 1. year of study, summer semester, 5 credits, compulsory-optional
    branch M-MET , 1. year of study, summer semester, 5 credits, compulsory

  • Programme M2I-P Master's

    branch M-AIŘ , 1. year of study, summer semester, 5 credits, compulsory
    branch M-AIŘ , 1. year of study, summer semester, 5 credits, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


1. Introduction to artificial intelligence.
2. Uninformed search in state space.
3. Informed search in state space.
4. Problem solving by decomposition into sub-problems, AND/OR search methods.
5. Game playing methods.
6. Predicate logic and resolution method. Non-traditional logics.
7. Horn logic and Prolog.
8. Knowledge representation by rules and corresponding methods of reasoning.
9. Non-rule and hybrid knowledge representation and corresponding methods of reasoning.
10. Classical approaches to handling uncertainty (pseudo-bayesian approach, certainty factors).
11. Theoretical approaches to handling uncertainty (bayesian nets, fuzzy approach).
12. Machine learning.
13. Agents and multiagent systems.

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer


1. Functional programming and Lisp.
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. Predicate logic and resolution method.
8. Logic programming and Prolog.
9. Rule-based programming and Clips.
10. Handling uncertainty in rule-based systems.
11. Bayesian nets.
12. Machine learning methods.
13. Presentation of semester projects.