Course details

Fundamentals of Artificial Intelligence

IZU Acad. year 2004/2005 Summer semester 4 credits

Current academic year

Problem solving methods using state space search (blind: BFS, DFS, DLS, IDS, BS, UCS, Backtracking, Forward checking; informed: BestFS, GS, A*, IDA, SMA, Hill Climbing, Simulated annealing, Heuristic repair). Problem solving methods using problem decomposition, AND/OR graphs. Games playing methods (minimax, alphabeta, games with uncertainty). Method of resolution. Implementations of basic search algorithms in PROLOG and LISP languages. Knowledge representation (logical, semantic, structural and procedural schemes of representation). Machine learning principles. Statistical and structural pattern recognition. Fundamentals of computer vision. Base principles of natural language processing.

Guarantor

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

Language of instruction

Czech, English

Completion

Credit+Examination

Time span

  • 26 hrs lectures
  • 13 hrs pc labs

Department

Department of Intelligent Systems (UITS)

Subject specific learning outcomes and competences

Students acquaint with problem solving methods using state space search and problem decomposition and with game playing methods. They acquire practical skills with creating of programs for problem solving using heuristics. Further they acquire basic knowledge about (machine) knowledge representation, method of resolution, machine learning and they acquaint with machine vision and natural language processing problematic.

Students acquire knowledge needed for solving of general problems in practice. Further they acquire basic knowledge of pattern recognition theory and of possibilities of practical using functional and logic languages.

Learning objectives

To acquaint students with fundamentals of artificial intelligence, namely with possible approaches to problem solving (blind and informed methods, game playing), with method of resolution and with implementations of basic search algorithms in PROLOG and LISP languages. Further with basic schemes of knowledge representation, with machine learning principles and with general theory of recognition. Students acquire base information about computer vision and natural language processing, too.

Prerequisite knowledge and skills

There are no prerequisites

Study literature

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7

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, third edition 2010, ISBN 0-13-604259-7
  • Luger,G.F.: Artificial Intelligence - Structures and strategies for Complex Problem Solving, 6th Edition,
    Pearson Education, Inc., 2009, ISBN-13: 978-0-321-54589-3, ISBN-10: 0-321-54589-3 

Syllabus of lectures

  1. Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS).
  2. Solving problem methods, cont. (BS, UCS, Backtracking, Forward checking).
  3. Solving problem methods, cont. (BestFS, GS, A*, IDA, SMA, Hill Climbing, Simulated annealing, 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. Knowledge representation (representational schemes).
  8. Implementation of basic search algorithms in PROLOG.
  9. Implementation of basic search algorithms in LISP.
  10. Machine learning.
  11. Fundamentals of pattern recognition theory.
  12. Principles of computer vision.
  13. Principles of natural language processing.

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

At least 15 points earned during semester.

Controlled instruction

There are no checked study.

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