Course details

Artificial Intelligence

UIN Acad. year 2005/2006 Summer semester 6 credits

Current academic year

Problem solving, state space search, problem decomposition, games playing. Knowledge representation. AI languages (PROLOG, LISP). Machine learning principles. Statistical and structural pattern recognition. Fundamentals of computer vision. Basic principles of natural language processing. Basic principles of expert systems.

Guarantor

Zbořil František, doc. Ing., CSc. (DITS)

Language of instruction

Czech

Completion

Examination

Time span

  • 39 hrs lectures
  • 12 hrs pc labs
  • 14 hrs projects

Department

Department of Intelligent Systems (UITS)

Subject specific learning outcomes and competences

Students acquire knowledge of various approaches of problem solving and basic information about machine learning, computer vision, natural language processing and expert systems. They will be able to create programs using heuristics for problem solving.

Learning objectives

To give the students the knowledge of fundamentals of artificial intelligence, namely knowledge of problem solving approaches, machine learning principles and general theory of recognitions. Students acquire base information about computer vision, natural language processing and expert systems.

Prerequisite knowledge and skills

None.

Study literature

  • Zbořil,F., Hanáček,P.: Umělá inteligence, Skripta VUT v Brně, VUT v Brně, 1990, ISBN 80-214-0349-7
  • Mařík,V., Štěpánková,O., Lažanský,J. a kol.: Umělá inteligence (1)+(2), ACADEMIA Praha, 1993 (1), 1997 (2), ISBN 80-200-0502-1

Fundamental literature

  • Russel,S., Norvig.,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2 
  • Luger,G.F., Stubblefield,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 1993, ISBN 0-8053-4785-2

Syllabus of lectures

  1. Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS)
  2. Solving problem methods, cont. (BS, UCS, Hill Climbing, Simulated annealing, Backtracking, Forward checking)
  3. Solving problem methods, cont. (GS, BestFS, A*, IDA, SMA, Heuristic repair)
  4. Solving problem methods, cont. (Problem decomposition, AND/OR graphs)
  5. Methods of game playing (minimax, alpha-beta, games with unpredictability)
  6. Logic and AI, resolution and it's application in problem solving
  7. Implementation of basic search algorithms in PROLOG
  8. Implementation of basic search algorithms in LISP
  9. Machine learning
  10. Fundamentals of pattern recognition theory
  11. Principles of computer vision
  12. Principles of natural language processing
  13. Principles of expert systems

Syllabus of computer exercises

  1. Problem solving - simple programs.
  2. Problem solving - games playing.
  3. PROLOG language - basic information.
  4. PROLOG language - simple individual programs.
  5. LISP language - basic information.
  6. LISP language - simple individual programs.
  7. Simple programs for pattern recognition.

Progress assessment

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Controlled instruction

  • Written mid-term exam
  • Project
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