Artificial Intelligence

A tantárgy neve magyarul / Name of the subject in Hungarian: Mesterséges intelligencia

Last updated: 2016. május 26.

Budapest University of Technology and Economics
Faculty of Electrical Engineering and Informatics
Engineering Information Technology
Course ID Semester Assessment Credit Tantárgyfélév
VIMIAC00 5 3/0/0/f 4  
3. Course coordinator and department Dr. Dobrowiecki Tadeusz Pawel,
Web page of the course
4. Instructors

Dr. Tadeusz Dobrowiecki, Department of Measurement and Information Systems

5. Required knowledge

  mathematical logic, probability theory, computer science basics

6. Pre-requisites
((TargyEredmeny("BMEVISZAA01" , "jegy" , _ ) >= 2 VAGY
TargyEredmeny("BMEVISZAA04" , "jegy" , _ ) >= 2 VAGY
TargyEredmeny("BMEVISZA110" , "jegy" , _ ) >= 2 )

VAGY Training.Code=("5NAA8") )

ÉS NEM ( TárgyEredmény( "BMEVIMIA313" , "jegy" , _ ) >= 2
TárgyEredmény("BMEVIMIA313", "FELVETEL", AktualisFelev()) > 0
TárgyEredmény( "BMEVIMIAC10" , "jegy" , _ ) >= 2
TárgyEredmény("BMEVIMIAC10", "FELVETEL", AktualisFelev()) > 0)

A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

A kötelező előtanulmányi rendek grafikus formában itt láthatók.


Mandatory: Theory of algorithms (completed)

Recommended: none

7. Objectives, learning outcomes and obtained knowledge

The aim of the subject is a short, yet substantial presentation of the field of artificial intelligence. The principal presented topics are (1) expressing intelligent behavior with computational models, (2) analysis and application of the formal and heuristic methods of artificial intelligence, (3) methods and problems of practical implementations. The subject is intended to develop the abilities and skills of the students of informatics in the area of:

- studying novel applications of the computing,

- developing effective methods to solve computational problems,

- understanding the technological and conceptual limits of the computer science,

- intellectual understanding of the central role of the algorithm in information systems.

8. Synopsis

Agent paradigm: Intelligent system and its environment. Formal modeling and solving of complex problems within agent paradigm. Comparing problem solving methods (search strategies). Heuristics for reducing complexity. Knowledge intensive approach and complexity. Experimenting with the scheduling problems: modeling within the paradigm and solving with the search algorithms.

Planning: Planning as a tool of problem solving. Basic representations for planning. The basics of the modern planning algorithms. Hierarchical and conditional planning. The question of the resource constraints. Integrated planning and execution. Experimenting with the assembly problems: developing plans taking into account various problems of increasing complexity.

Knowledge intensive systems. Formal representation and manipulation of knowledge. Logic based methods. Using first order logic to describe problems and to compute solutions. The functioning of rule-based systems. Inference methods for uncertain knowledge. Probabilistic inference systems. Representing vague meaning with fuzzy sets. Experimenting with the diagnostic problem with knowledge of different levels of uncertainty, using suitable methods, or experimenting with building a fuzzy system (rule-based language, fuzzy software packages, etc.).

Learning. Learning within agent paradigm. Inductive logical learning (decision trees, learning general logical expressions). Learning in neural and Bayesian networks. Reinforcement learning. Genetic algorithms and evolutionary programming. Experimenting with multiple learning problems, using suitable software packages.

9. Method of instruction lecturing
10. Assessment

During the term:

one midterm exam (scheduled beyond the weekly lecture), which can be reinstated acc. to the Code of Studies and Exams. The required minimum level is 40% (i.e. 20 points).

Home assignment, to be requested from a Home Work server. Further assignment scheduling is available from the server. The grading of the assignment is „not satisfactory" (0 point), or „satisfactory" (6-20 points). The accomplishment of the subject is based on the minimal 40% fulfillment of the midterm exam and a satisfactory home work assignment.
11. Recaps

Acc. to the Code of Studies and Exams.

12. Consultations optional, on demand
13. References, textbooks and resources

Stuart Russell and Peter Norvig: Artificial Intelligence. The Modern Approach, Prentice-Hall, 1995

Stuart Russell and Peter Norvig: Artificial Intelligence. The Modern Approach, 2nd ed. Prentice-Hall, 2000

14. Required learning hours and assignment
Contact lecture-hour
Midterm preparation for lectures25
Preparation for midterm exam25
Home work assignment
15. Syllabus prepared by Dr. Tadeusz Dobrowiecki, Department of Measurement and Information Systems