Basics of Artificial Intelligence

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Laser Physics and Photonics

Synopsis:

This course provides introduction to symbolic artificial intelligence. It presents the algorithms for informed and non-informed state space search, nontraditional methods of problem solving, knowledge representation by means of formal logic, methods of automated reasoning and introduction to markovian decision making.

Requirements:

Syllabus of lectures:

1. Introduction to artificial intelligence.

2. Problem solving using state space search.

3. Non-informed state space search.

4. Informed state space search - A* algorithm.

5. Nontraditional state space search methods.

6. Knowledge representation and rule-based systems reasoning.

7. Introduction to two-player games.

8. Logics and knowledge representation.

9. Reasoning in first-order predicate logic, situation calculus.

10. Introduction to uncertainty in knowledge representation. Markov models.

11. Markov chains and decision processes.

12. Modal logic - definitions and applications.

13. Temporal logic - definitions and applications.

14. Back-up class.

Syllabus of tutorials:

1. Non-informed state space search.

2. Informed state space search.

3. A* algorithm.

4. Constraint satisfaction problem.

5. Two-player games.

6. Two-player games.

7. Genetic algorithms and neural networks.

8. Review of mathematical logic, resolution principle.

9. Automated theorem provers.

10. Markov chains and decision processes.

11. Markov Decision Process toolbox.

12. Modal logic - examples.

13. Temporal logic - examples.

14. Back-up class, credits.

Study Objective:

Knowledge:

Basic knowledge of artificial intelligence.

Skills:

Ability to apply artificial intelligence methods.

Study materials:

Key references:

[1] S. Russell and P. Norvig: Artificial Intelligence: A Modern Approach, Prentice Hall, Second Edition, 2003

Recommended references:

[2] V. Mařík, O. Štěpánková, J. Lažanský: Umělá inteligence 1, Academia, 2000.

[3] R. Brachman, H. Levesque: Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: