Course detail
Artificial Intelligence Algorithms
FSI-VAI-A Acad. year: 2022/2023 Summer semester
The course introduces basic approaches to artificial intelligence algorithms and classical methods used in the field. Main emphasis is given to automated formulas proves, knowledge representation and problem solving. Practical use of the methods is demonstrated on solving simple engineering problems.
Language of instruction
English
Number of ECTS credits
4
Supervisor
Department
Learning outcomes of the course unit
Understanding of basic methods of artificial intelligence and ability of their implementation.
Prerequisites
Knowledge of algorithmization, programming and the basics of mathematical logic and probability theory are assumed.
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes
Course-unit credit requirements: passing partial tests and submitting a functional software project which uses implementation of selected AI method. Student can obtain 100 marks, 40 marks during seminars (20 for tests and 20 for project; he needs at least 20), 60 marks during exam (he needs at least 30).
Aims
The course objective is to make students familiar with basic resources of artificial intelligence, potential and adequacy of their use in engineering problems solving.
Specification of controlled education, way of implementation and compensation for absences
The attendance at lectures is recommended, at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a tutor.
The study programmes with the given course
Programme N-MAI-A: Mathematical Engineering, Master's, compulsory-optional
Type of course unit
Lecture
26 hours, optionally
Syllabus
1. Introduction to artificial intelligence.
2. State space, uninformed search.
3. Informed search in state space.
4. Problem solving by decomposition into sub-problems, AND/OR search methods.
5. Game playing methods.
6. Constraint satisfaction problems.
7. Predicate logic and resolution method.
8. Horn logic and logic programming.
9. Non-traditional logics.
10. Knowledge representation.
11. Representation and processing of uncertainty.
12. Bayesian and decision networks.
13. Markov decision processes.
Computer-assisted exercise
26 hours, compulsory
Syllabus
1. Introductory motivational examples.
2. Uninformed methods of state space search.
3. Informed methods of state space search.
4. A* algorithm and its modifications.
5. Methods of AND/OR graph search.
6. Constraint satisfaction problems.
7. Game playing methods.
8. Predicate logic and resolution method.
9. Logic programming and Prolog.
10. Solving AI problems in Prolog.
11. Production and expert systems.
12. Bayesian networks.
13. Presentation of semester projects.