Artificial Intelligence Fundamentals

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

Students are introduced to the fundamental problems in the Artificial Intelligence, and the basic methods for their solving. It focuses mainly on the classical tasks from the areas of state space search, multi-agent systems, game theory, planning, and machine learning. Modern soft-computing methods, including the evolutionary algorithms and the neural networks, will be presented as well.

Requirements:

Basic knowledge of statistics, algebra and algorithmization. Programming capabilities.

Syllabus of lectures:

1. Introduction to Artiffcial Intelligence and its history. Turing test, rational behavior and reasoning.

2. The state space and the heuristic methods for state space exploration.

3. Advanced state space search methods: Hill climbing, Simulated annealing, tabu search, population-based methods.

4. Evolutionary computation techniques. Genetic algorithm, operators of initialization, crossover, mutation, and reproduction.

5. Genetic programming, evolution of tree structures. Crossover and mutation of subtrees.

6. Constraint satisfaction problems and the heuristics for their solving.

7. Automated planning. Planning state space search, plans, and actions. Relaxation and abstraction in planning.

8. Multi-agent system and their architectures. Relations between the world and the agents, agent types, utility functions.

9. Game theory. Games in the normal form, game analysis. Pareto-optimality, Nash equilibrium.

10. Game in the extensive form, methods for searching the game tree. Minimax algorithm, alpha-beta pruning.

11. Introduction to Machine learning and Data mining. Supervised and unsupervised learning. Classification, regression, and cluster analysis.

12. Artificial neural networks. Perceptron networks, activation function, backpropagation algorithm, self-organizing networks.

13. Other computational intelligence methods, modern trends.

Syllabus of tutorials:

1. Interactive tools for artificial intelligence

2. AI problem set 1

3. AI problem set 2

4. Programming assignment 1

5. Consulting assignment 1

6. AI problem set 3

7. AI problem set 4

8. Programming assignment 2

9. Consulting assignment 2

10. AI problem set 5

11. Programming assignment 3

12. Consulting assignment 3

13. Reserved, credit

Study Objective:

The course aims to offer students a survey to the areas of Artificial Intelligence. Its main objective is to present a comprehensive overview of AI problems, rather than examining individual methods in detail.

Study materials:

1. Russel S., Norvig P. : Artfcial Intelligence: A Modern Approach (4th Edition). Prentice Hall, 2020. ISBN 978-0134610993.

2. Ghallab M., Nau D., Traverso P. : Automated Planning and Acting. Cambridge University Press, 2016. ISBN 978-1107037274.

3. Lažanský J., Mařík V., Štěpánková O. : Umělá inteligence (1) - (6). Academia, 2013. ISBN 978-80-200-2267-9.

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Bachelor Specialization Information Security, part-time, in Czech, 2021 (compulsory elective course)