Artificial Intelligence Algorithms (FSI-VAI)

Academic year 2021/2022
Supervisor: prof. Ing. Radomil Matoušek, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech
Aims of the course unit:
The course objective is to make students familiar with basic resources of artificial intelligence, potential and adequacy of their use in engineering problems solving.
Learning outcomes and competences:
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.
Course contents:
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.
Teaching methods and criteria:
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).
Controlled participation in lessons:
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.
Type of course unit:
    Lecture  13 × 2 hrs. optionally                  
    Computer-assisted exercise  13 × 2 hrs. compulsory                  
Course curriculum:
    Lecture 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 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.
Literature - fundamental:
1. Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021.
2. Negnevitsky, M. Artificial Intelligence. A Guide to Intelligent Systems. Pearson Education 2011.
3. Bratko, I. Prolog Programming for Artificial Intelligence. Pearson Education Canada 2011.
4. Luger, G.F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison-Wesley 2008.
Literature - recommended:
3. Mařík, V. a kol. Umělá inteligence 1 - 6. Praha, Academia.
4. Poole, D.L. and Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press 2023. https://artint.info/3e/html/ArtInt3e.html
The study programmes with the given course:
Programme Study form Branch Spec. Final classification   Course-unit credits     Obligation     Level     Year     Semester  
N-AIŘ-P full-time study --- no specialisation -- Cr,Ex 4 Compulsory 2 1 S
N-MAI-P full-time study --- no specialisation -- Cr,Ex 4 Compulsory-optional 2 1 S