Artificial Intelligence Algorithms
FSI-VAIAcad. year: 2017/2018
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.
Learning outcomes of the course unit
Understanding of basic methods of artificial intelligence and ability of their implementation.
The knowledge of basic relations of the graphs theory and object oriented technologies is expected.
Recommended optional programme components
Recommended or required reading
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Harlow, Addison-Wesley 2005. (EN)
Edward A. Bender: Mathematical Methods in Artificial Intelligence. IEEE Computer Society Press 1996. (EN)
Zbořil, F. a kol.: Umělá inteligence (skriptum VUT). (CS)
Russel, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall 2009. (EN)
Mařík, V. a kol.: Umělá inteligence. Praha, Academia. (CS)
Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley 2008. (EN)
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: submitting a functional software project which uses implementation of selected AI method. Project is specified in the first seminar. Systematic checks and consultations are performed during the semester. Each student has to get through one test and complete all given tasks. Student can obtain 100 marks, 40 marks during seminars (20 for project and 20 for test; he needs at least 20), 60 marks during exam (he needs at least 30).
Language of instruction
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.
Classification of course in study plans
- Programme M2A-P Master's
- Programme M2I-P Master's
Type of course unit
26 hours, optionally
Teacher / Lecturer
1. Introduction, AI areas.
2. Uninformed search in state space.
3. Informed search methods.
4. Knowledge representation by rules, production systems.
5. Evolutionary search methods.
6. Problem solving by decomposition into sub-problems, AND/OR search methods.
7. Game playing methods.
8. Knowledge representation by predicate logic formulas, resolution method.
9. Horn logic and Prolog. Non-traditional logics.
10. Knowledge representation by semantic networks, frames, scripts and objects.
11. Machine learning.
12. Intelligent and reactive agents.
13. Multiagent systems.
26 hours, compulsory
Teacher / Lecturer
1. Uninformed methods of state space search - theoretical analysis.
2. Uninformed methods of state space search - implementation using object oriented programming.
3. Informed methods of state space search - gradient algorithm, Dijkstra’s algorithm, best-first search algorithm, theoretical analysis.
4. A-star algorithm - theoretical analysis, implementation using object oriented programming.
5. Solving problems by means of genetic algorithms.
6. Decomposition of a problem into sub-problems, AND-OR graph.
7. Object design and implementation of AND/OR graph.
8. Game playing methods, minimax, alpha-beta pruning.
9. Intermediate test.
10. Predicate logic formulas, resolution method.
11. Solving AI problems by means of Prolog.
12. Solving a selected practical problem by means of AI.
13. Presentation of semester projects.