Course detail

Artificial Intelligence Algorithms

FSI-VAIAcad. year: 2015/2016

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

5

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

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. Uninformed search in state space.
3. Informed search methods.
4. Evolutionary search methods.
5. Knowledge representation by rules, production systems.
6. Problem solving by decomposition into sub-problems, AND/OR search methods.
7. Game playing methods.
8. Knowledge representation by predicate logic formulas, resolution method.
9. Horn logic and Prolog. Non-traditional logics.
10. Knowledge representation by semantic networks, frames, scripts and objects.
11. Machine learning.
12. Intelligent and reactive agents.
13. Multiagent systems.

Computer-assisted exercise

26 hod., compulsory

Teacher / Lecturer

Ing. Kamil Miškařík

Syllabus

1. Uninformed methods of state space search - theoretical analysis.
2. Uninformed methods of state space search - implementation using object oriented programming.
3. Informed 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.
5. Solving problems by means of genetic algorithms.
6. Decomposition of a problem into sub-problems, AND-OR graph.
7. Object design and implementation of AND/OR graph.
8. Game playing methods, minimax, alpha-beta pruning.
9. Intermediate test.
10. Predicate logic formulas, resolution method.
11. Solving AI problems by means of Prolog.
12. Solving a selected practical problem by means of AI.
13. Presentation of semester projects.