Subject description - B4M36MAS

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B4M36MAS Computational Game Theory
Roles:PO Extent of teaching:2P+2C
Department:13136 Language of teaching:CS
Guarantors:Pěchouček M. Completion:Z,ZK
Lecturers:Jakob M., Kroupa T., Kubíček O., Votroubek T. Credits:6
Tutors:Jakob M., Kroupa T., Kubíček O., Votroubek T. Semester:Z

Web page:

https://cw.fel.cvut.cz/wiki/courses/B4M36MAS

Anotation:

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.

Study targets:

By the end of the course, you will be equipped with the knowledge and skills to analyze complex strategic situations, evaluate fairness of allocation mechanisms, and appreciate the exciting applications of game theory in AI.

Content:

Normal-form games. Extensive-form games. Equilibria (Nash, Stackelberg, correlated). Bayesian games and auctions. Coalitional games and their solutions.

Course outlines:

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.

Exercises outline:

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.

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

Requirements:

- programming in Python - optimization, in particular linear programming basics - linear algebra - probability and statistics - discrete mathematics

Subject is included into these academic programs:

Program Branch Role Recommended semester
MPOI7_2018 Artificial Intelligence PO 1

Page updated 15.9.2024 17:51:20, semester: Z/2023-4, Z/2025-6, L/2023-4, Z,L/2024-5, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)
Responsible person: prof. Ing. Jiří Jakovenko, Ph.D.