Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2018/2019

Problem solving: State space search (BFS, DFS, DLS, IDS, BS, UCS, Backtracking, Forward checking, Min-conflict, BestFS, GS, A*, Hill Climbing, Simulated Annealing methods). Problem decomposition (AND/OR graphs). Solving optimization problems by nature-inspired algorithms (GA, ACO and PSO). Games playing (Mini-Max and Alfa-Beta algorithms). Logic and artificial intelligence (method of resolution and its utilization for task solving and planning). PROLOG language and implementations of basic search algorithms in this language. Machine learning principles. Classification and patterns recognition. Basic principles of expert systems. Fundamentals of computer vision.  Principles of natural language processing. Introduction into agent systems.

Learning outcomes of the course unit

  • Students will learn terminology in Artificial Intelligence field both in Czech and in English language.
  • Students will learn read and so partly write programs in PROLOG language.

  • Students will acquaint with problem solving methods based on state space search and on decomposition problem into sub-problems.
  • Students will acquaint with basic game playing methods.
  • Students will learn to solve optimization problems.
  • Students will acquaint with fundamentals of propositional and predicate logics and with their applications.
  • Students will learn how to use basic methods of machine learning, classification and recognition.
  • Students will acquaint with fundamentals of expert systems, machine vision and natural language processing.
  • Students will acquaint with fundamentals of multiagent systems.

Prerequisites

  • Basic knowledge of the programming.
  • Knowledge of secondary school level mathematics.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

  • 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

  • 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 

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

  • Mid-term written examination - 20 points.
  • Programs in computer exercises - 20 points.
  • Final written examination - 60 points; The minimal number of points which can be obtained from the final written examination is 25. Otherwise, no points will be assigned to a student.

Exam prerequisites:
At least 15 points earned during semester (mid-term test + tasks in computer exercises).

Language of instruction

Czech, English

Work placements

Not applicable.

Course curriculum

    Syllabus of lectures:
    1. Introduction, Artificial Intelligence (AI) definition, types of AI problems, solving problem methods.
    2. State space search methods.
    3. Solving methods using decomposition problems into sub-problems (AND/OR graphs).
    4. Solving of optimization problems using algorithms inspired by nature - short introduction into Genetic algorithms, ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization).
    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. Classical classifiers, perceptron.
    11. Expert systems.
    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.

Aims

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

Specification of controlled education, way of implementation and compensation for absences

Missed
lessons (exercises and tests) can be substituted only exceptionally, after proving
that the absences had legitimate reasons.

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2. year of study, summer semester, 4 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

  1. Introduction, Artificial Intelligence (AI) definition, types of AI problems, solving problem methods.
  2. State space search methods.
  3. Solving methods using decomposition problems into sub-problems.
  4. Solving optimization problems using algorithms inspired by nature.
  5. Basic methods of game playing.
  6. Logic and AI, resolution and it's application in problem solving and planning.
  7. PROLOG language and its use in AI.
  8. Machine learning.  
  9. Classification and pattern recognition.
  10. Principles of expert systems.
  11. Principles of computer vision.
  12. Principles of natural language processing.
  13. Introduction into agent systems.

Computer exercise

13 hours, compulsory

Teacher / Lecturer

Syllabus

  1. Problem solving - State Space Search.
  2. Problem solving - CSP.
  3. Problem solving - game playing.
  4. Predicate logic - method of resolution.
  5. PROLOG language - basic information.
  6. PROLOG language - simple individual programs.
  7. Simple programs for pattern recognition.

eLearning

eLearning: opened course