Code: BIE-ZUM.21 |
Artificial Intelligence Fundamentals |
Lecturer: prof. RNDr. Pavel Surynek Ph.D. |
Weekly load: 2P+2C |
Completion: A, EX |
Department: 18105 |
Credits: 5 |
Semester: S |
- Description:
-
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.
- Contents:
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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.
- Seminar contents:
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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
- Recommended literature:
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1. Russel S., Norvig P. : Artificial 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.
- Keywords:
- Artificial Intelligence, State space, Heuristic search, Evolutionary Algorithms, Agent, Multi-agent system, Reasoning, Planning, Machine learning, Neural networks.
Abbreviations used:
Semester:
- W ... winter semester (usually October - February)
- S ... spring semester (usually March - June)
- W,S ... both semesters
Mode of completion of the course:
- A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
- GA ... Graded Assessment (a grade is awarded for this course)
- EX ... Examination (a grade is awarded for this course)
- A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)
Weekly load (hours per week):
- P ... lecture
- C ... seminar
- L ... laboratory
- R ... proseminar
- S ... seminar