Course detail

Artificial Intelligence Algorithms

FSI-VAIAcad. year: 2013/2014

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. RNDr. Ing. Tomáš Březina, CSc.

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

The knowledge of basic relations of the graphs theory and object oriented technologies is expected.

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: submitting a functional software project which uses implementation of selected AI method. Project is specified in the first seminar. Systematic checks and consultations are performed during the semester. Each student has to get through one test and complete all given tasks. Student can obtain 100 marks, 40 marks during seminars (20 for project and 20 for test; 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)

Classification of course in study plans

  • Programme N3901-2 Master's

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

  • Programme N2301-2 Master's

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

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

RNDr. Jiří Dvořák, CSc.

Syllabus

1. Introduction, AI areas.
2. Problems solving: search in state space.
3. Problems solving: decomposition into sub-problems, games playing methods.
4. Formal logic systems, propositional and predicate logic.
5. Generalized resolution method.
6. Predicate logic and Prolog. Non-traditional logics.
7. Knowledge representation: predicate logic formulas and rules.
8. Knowledge representation: semantic networks, frames and scenarios. Declarative and procedural representation.
9. Machine learning.
10. Evolution techniques.
11. Intelligent and reactive agents.
12. Multiagent systems.
13. Other AI areas. Actual state, prospects.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Vítězslav Popovský

Syllabus

1. Blind methods of state space search - breadth first search, depth first search, theoretical analysis.
2. Blind methods of state space search - implementation of selected algorithms using object oriented programming under .NET framework.
3. Heuristic methods of state space search - gradient algorithm, Dijkstra’s algorithm, best-first search algorithm, theoretical analysis.
4. A-star algorithm - theoretical analysis, implementation using object oriented programming under .NET framework.
5. Problem solving: implementation of concrete heuristic algorithm.
6. Decomposition of a problem into sub-problems, AND-OR graph.
7. Object design and implementation of AND-OR graph.
8. Games playing methods, minimax, alpha-beta pruning.
9. Solving AI problems by means of Prolog.
10. Intermediate test.
11. Solving problems by means of genetic algorithms.
12. Solving a selected practical problem by means of AI.
13. Presentation of semester projects.