Artificial Intelligence Algorithms (FSI-VAI)

Academic year 2024/2025
Supervisor: prof. Ing. Radomil Matoušek, Ph.D.  
Supervising institute: ÚAI all courses guaranted by this institute
Teaching language: Czech
Course type: departmental course
Aims of the course unit:

Knowledge of the basic means of artificial intelligence and the possibilities of their use in solving engineering tasks.
Understanding of basic methods of artificial intelligence and ability of their implementation.
Learning outcomes and competences:
 
Prerequisites:
 
Course contents:
 
Teaching methods and criteria:
 
Assesment methods and criteria linked to learning outcomes:

Course-unit credit requirements: Creation of functional software projects using some of the discussed AI methods and working out a presentation of some undiscussed AI method. Student can obtain 100 marks, 40 marks during seminars (30 for projects and 10 for the presentation; 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 teacher.
Controlled participation in lessons:
 
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. Representation, use and learning of knowledge.
10. Representation and processing of uncertainty.
11. Bayesian and decision networks.
12. Non-traditional logics.
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. Game playing methods.
7. Constraint satisfaction problems.
8. Predicate logic and resolution method.
9. Logic programming and Prolog.
10. Solving AI problems in Prolog.
11. Learning symbolic knowledge.
12. Bayesian networks.
13. Probabilistic and fuzzy logic programming.
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 W
N-MAI-P full-time study --- no specialisation -- Cr,Ex 4 Compulsory-optional 2 1 W
C-AKR-P full-time study CZS -- Cr,Ex 4 Elective 1 1 W