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    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 https://www.mit.bme.hu/eng/oktatas/targyak/vimiac00
    4. Instructors

    Dr. Tadeusz Dobrowiecki, Department of Measurement and Information Systems

    5. Required knowledge

      mathematical logic, probability theory, computer science basics

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

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

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

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

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    Ajánlott:

    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
    		45
    Midterm preparation for lectures25
    Preparation for midterm exam25
    Home work assignment
    		25
      
      
    Summary
    		120
    15. Syllabus prepared by Dr. Tadeusz Dobrowiecki, Department of Measurement and Information Systems