Course detail

Artificial Intelligence Algorithms

FSI-VAI-AAcad. year: 2021/2022

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

English

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

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)
Russel, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall 2010. https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf (EN)

Classification of course in study plans

  • Programme N-MAI-A Master's 1 year of study, summer semester, compulsory-optional

Type of course unit

 

Lecture

26 hod., optionally

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

Computer-assisted exercise

26 hod., compulsory

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.