Course detail

Artificial Intelligence Algorithms

FSI-VAI-KAcad. year: 2023/2024

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: 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).
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.

Aims

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

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

Recommended reading

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

eLearning

eLearning: currently opened course

Classification of course in study plans

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

Type of course unit

 

Guided consultation in combined form of studies

17 hours, compulsory

Teacher / Lecturer

RNDr. Jiří Dvořák, CSc.

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. Non-traditional logics.
10. Knowledge representation.
11. Representation and processing of uncertainty.
12. Bayesian and decision networks.
13. Markov decision processes.

Guided consultation

35 hours, 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. Constraint satisfaction problems.
7. Game playing methods.
8. Predicate logic and resolution method.
9. Logic programming and Prolog.
10. Solving AI problems in Prolog.
11. Production and expert systems.
12. Bayesian networks.
13. Presentation of semester projects.

eLearning

eLearning: currently opened course