Course detail

Artificial Intelligence Algorithms

FSI-VAIAcad. year: 2020/2021

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

Mode of study

Not applicable.

Guarantor

prof. Ing. Radomil Matoušek, Ph.D.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

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

Prerequisites

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

Co-requisites

Not applicable.

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).

Course curriculum

Not applicable.

Work placements

Not applicable.

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.

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.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Bratko, I. Prolog Programming for Artificial Intelligence. Pearson Education Canada 2011. (EN)
Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley 2008. (EN)
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Pearson Education 2011. (EN)
Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021. (EN)

Recommended reading

Mařík, V. a kol. Umělá inteligence 1 - 6. Praha, Academia. (CS)
Poole, D.L. and Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press 2023. https://artint.info/3e/html/ArtInt3e.html (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MAI , 1 year of study, summer semester, compulsory-optional

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

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Ing. Radomil Matoušek, Ph.D.
RNDr. Jiří Dvořák, CSc.

Syllabus

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 hod., compulsory

Teacher / Lecturer

RNDr. Jiří Dvořák, CSc.
Ing. Petr Šoustek, Ph.D.

Syllabus

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.

Elearning

eLearning: currently opened course