Course detail

Artificial Intelligence Algorithms

FSI-VAIAcad. year: 2010/2011

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 classification. 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. RNDr. Ing. Tomáš Březina, CSc.

Department

Institute of Automation and Computer Science (ÚAI)

Learning outcomes of the course unit

Detailed introduction to basic methods of artificial intelligence and 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

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

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 week of term. Systematic checks and consultations are performed during the term. Each student have to get through two tests and complete all excersises. Student can obtain 100points (40 points during the term, 60 points during exam).

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

Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021. (EN)
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Pearson Education 2011. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme M2A-P Master's

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

  • Programme M2I-P 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 hours, optionally

Teacher / Lecturer

RNDr. Jiří Dvořák, CSc.

Syllabus

1. Introduction, AI domain.
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. Text analysis. Morphologic, syntactic, semantic a pragmatic analysis. Grammars use.
10. Features and structural recognition. Grammars use.
11. Computer vision. Topologic aspects of image, structural analysis. Scenes analysis with polyhedrons.
12. Speech recognition. Acoustic signal transformation, filtration analysis, clipped speech method. Segmentation and segment classification.
13. Other AI areas. Actual state, prospects.

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer

Ing. Vít Ondroušek, Ph.D.

Syllabus

1.Blind methods od state space search - breadth first search, depth first search, theoretical analysis
2.Blind methods od state space search - implementation of selected algorithm using object oriented programming under .NET framework
3.Heuristic methods od state space search - gradient algorithm, dijkstra algorithm, best first search algorithm, theoretical analysis
4.A-star algorithm - theoretical analysis, implementation using object oriented programming under .NET framework.
5.Summary test
6.Problems solving: implementation of concrete heuristic algorithm I,
7.Problems solving: implementation of concrete heuristic algorithm II,
8.Decomposition into sub-problems, games playing methods, minimax - theretical analysis.
9.Alpha-beta prunning.
10.Design and realization of object implementation of AND-OR graph, binary and numeric evaluation.
11.Summary test
12.Problem oriented implementation of selected algorithm using object oriented programming under .NET framework (playing games algorithms, prolog)
13.Term project defence.