Course detail
Artificial Intelligence Algorithms
FSI-VAIAcad. year: 2010/2011
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 classification. Practical use of the methods is demonstrated on solving simple engineering problems.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Russel, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education 2021. (EN)
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Problems solving: search in state space.
3. Problems solving: decomposition into sub-problems, games playing methods.
4. Formal logic systems, propositional and predicate logic.
5. Generalized resolution method.
6. Predicate logic and Prolog. Non-traditional logics.
7. Knowledge representation: predicate logic formulas and rules.
8. Knowledge representation: semantic networks, frames and scenarios. Declarative and procedural representation.
9. Text analysis. Morphologic, syntactic, semantic a pragmatic analysis. Grammars use.
10. Features and structural recognition. Grammars use.
11. Computer vision. Topologic aspects of image, structural analysis. Scenes analysis with polyhedrons.
12. Speech recognition. Acoustic signal transformation, filtration analysis, clipped speech method. Segmentation and segment classification.
13. Other AI areas. Actual state, prospects.
Computer-assisted exercise
Teacher / Lecturer
Syllabus
2.Blind methods od state space search - implementation of selected algorithm using object oriented programming under .NET framework
3.Heuristic methods od state space search - gradient algorithm, dijkstra algorithm, best first search algorithm, theoretical analysis
4.A-star algorithm - theoretical analysis, implementation using object oriented programming under .NET framework.
5.Summary test
6.Problems solving: implementation of concrete heuristic algorithm I,
7.Problems solving: implementation of concrete heuristic algorithm II,
8.Decomposition into sub-problems, games playing methods, minimax - theretical analysis.
9.Alpha-beta prunning.
10.Design and realization of object implementation of AND-OR graph, binary and numeric evaluation.
11.Summary test
12.Problem oriented implementation of selected algorithm using object oriented programming under .NET framework (playing games algorithms, prolog)
13.Term project defence.