Course detail

Artificial Intelligence Algorithms

FSI-VAI-KAcad. year: 2011/2012

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. 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

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 seminar. Systematic checks and consultations are performed during the semester. Each student has to get through two tests and complete all given tasks. Student can obtain 100 marks (40 marks during seminars, 60 marks 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

Kim W.Tracy, Peter Bouthoorn: Object-oriented Artificial Intelligence Using C++
Edward A. Bender: Mathematical Methods in Artificial Intelligence

Recommended reading

F.Zbořil a kol.: Umělá inteligence (skriptum VUT)

Classification of course in study plans

  • Programme M2I-K 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

 

Guided consultation

17 hours, 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.

Controlled Self-study

35 hours, compulsory

Teacher / Lecturer

prof. RNDr. Ing. Tomáš Březina, CSc.

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. Intermediate test.
6. Problem solving: implementation of concrete heuristic algorithm.
7. Decomposition of a problem into sub-problems, AND-OR graph, object design and implementation.
8. Games playing methods, minimax, alpha-beta pruning.
9. Solving AI problems by means of Prolog.
10. Solving problems by means of genetic algorithms.
11. Intermediate test.
12. Solving a selected practical problem by means of AI.
13. Presentation of semester projects.