Code: BE4M36MAS Computational Game Theory
Lecturer: prof. Dr. Michal Pěchouček MSc. Weekly load: 2P+2C Completion: A, EX
Department: 13136 Credits: 6 Semester: W
Description:
This course is designed to introduce students to the fundamental concepts and applications of game theory, a powerful tool used to model strategic interactions among individuals, organizations, or countries. Throughout the course, we will delve into various aspects of game theory and explore its wide-ranging applications in diverse fields, including machine learning and AI.
Contents:
1. Introduction. Normal-form games.
2. Nash equilibria for normal-form games.
3. Tractable classes of games. Learning in games.
4. Extensive-form games.
5. Solving imperfect information EFGs.
6. Alternatives to NE.
7. Bayesian games
8. Auctions 1.
9. Auctions 2.
10. Coalitional games. The core.
11. The Shapley value.
12. Weighted voting games.
13. Games in computer science and ML.
14. Summary.
Seminar contents:
1. Introduction. Normal-form games.
2. Nash equilibria for normal-form games.
3. Tractable classes of games. Learning in games.
4. Extensive-form games.
5. Solving imperfect information EFGs.
6. Alternatives to NE.
7. Bayesian games
8. Auctions 1.
9. Auctions 2.
10. Coalitional games. The core.
11. The Shapley value.
12. Weighted voting games.
13. Games in computer science and ML.
14. Summary.
Recommended literature:
Shoham, Y. and Leyton-Brown, K.: Multiagent Systems. Cambridge University Press, 2008.
Maschler, M., Zamir, S., and Solan, E. Game Theory. Cambridge University Press, 2020.
Kochenderfer M.J., Wheeler T.A., Wray K.H. Algorithms for decision making. MIT press, 2022.
https://cw.fel.cvut.cz/b231/_media/courses/cgt/cgt_exercises.pdf
Keywords:
normal-form game, extensive-form game, Nash equilibrium, Stackelberg equilibrium, correlated equilibrium, Bayesian game, auction, coalitional game, Shapley value, voting game

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