Course detail
Artificial Intelligence Algorithms
FSI-VAI-AAcad. year: 2023/2024
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
Mode of study
Not applicable.
Guarantor
Entry knowledge
Knowledge of algorithmization, programming and the basics of mathematical logic and probability theory are assumed.
Rules for evaluation and completion of the course
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).
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 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.
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.
Understanding of basic methods of artificial intelligence and ability of their implementation.
Understanding of basic methods of artificial intelligence and ability of their implementation.
Study aids
Not applicable.
Prerequisites and corequisites
Not applicable.
Basic literature
Bratko, I. Prolog Programming for Artificial Intelligence. Pearson Education Canada 2011. (EN)
Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley 2008. (EN)
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Pearson Education 2011. (EN)
Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021. (EN)
Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley 2008. (EN)
Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Pearson Education 2011. (EN)
Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021. (EN)
Recommended reading
Poole, D.L. and Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press 2023. https://artint.info/3e/html/ArtInt3e.html (EN)
Russel, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall 2010. https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf (EN)
Russel, S., Norvig, P.: Artificial Intelligence. A Modern Approach. Prentice Hall 2010. https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf (EN)
Classification of course in study plans
- Programme N-MAI-A Master's 1 year of study, summer semester, compulsory-optional
Type of course unit
Lecture
26 hod., optionally
Teacher / Lecturer
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.
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 hod., compulsory
Teacher / Lecturer
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.
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.