Subject description - BE4M36MAS
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BE4M36MAS | Computational Game Theory | ||
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Roles: | PO | Extent of teaching: | 2P+2C |
Department: | 13136 | Language of teaching: | EN |
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/BE4M36MASAnotation:
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.pdfRequirements:
- programming in Python - optimization, in particular linear programming basics - linear algebra - probability and statistics - discrete mathematicsKeywords:
normal-form game, extensive-form game, Nash equilibrium, Stackelberg equilibrium, correlated equilibrium, Bayesian game, auction, coalitional game, Shapley value, voting game Subject is included into these academic programs:Program | Branch | Role | Recommended semester |
MEOI7_2018 | Artificial Intelligence | PO | 1 |
Page updated 19.4.2024 05:51:14, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |