Algorithmic Game Theory
Algorithmic Game Theory
Course with SWS 2/2/0 (lecture/exercise/practical) in SS 2026
Lecturer
Tutor
SWS
- 2/2/0
Modules
Examination method
- Written exam
- Oral exam
Game Theory is a multi-disciplinary and pervasive field that is concerned with how strategic decision making can be formally modelled and mathematically analysed.
In this course, we will approach the subject from a computer science perspective and – in addition to covering the foundational aspects – also address how game theory can be approached computationally, e.g. consider how computers can be programmed to play games, or analyse the computational complexity of various game-theoretic notions.
Dates and times
The lecture takes place as follows:
- Mondays, DS3, BEY/E39/U
Exercise sessions are offered at the following times:
- Thursdays, DS2
- Thursdays, DS4,
- Thursdays, DS5,
- Fridays, DS2.
Exercises start in the week of the first lecture, i.e. on 16th April.
Topics
- Noncooperative games in normal form
- Noncooperative games in extensive form
- Search in game trees
- Games with missing information
- Evolutionary game theory
- The Game Description Language and General Game Playing
- Cooperative Games
Exam
For CMS students and students wishing to use this course for modules INF-B-510 or INF-B-520, there will be a written exam (90min). The exam will be closed book, i.e. without notes, and no other resources (in particular technical aids) are permitted.
For anyone else (INF-VERT-2/6, INF-BAS-2/6, INF-PM-FOR, IST) the exam will be oral. To obtain an exam slot, please contact the CL group's secretary.- Jörg Rothe (Ed.): Economics and Computation. An Introduction to Algorithmic Game Theory, Computational Social Choice, and Fair Division. Springer-Verlag Berlin Heidelberg (2016) (Part I: Playing Successfully)
- Lectures 1, 2, 6, 7, 12, and 13
- Richard Alan Gillman, David Housman: Game Theory. A Modeling Approach. CRC Press (2019)
- Lectures 1, 2, 4, and 10
- Stuart J. Russell, Peter Norvig: Artificial Intelligence. A Modern Approach (Global Edition). Pearson (2021) (Chapter 6: Adversarial Search and Games)
- Lectures 5 and 6
- Todd W. Neller, Marc Lanctot: An Introduction to Counterfactual Regret Minimization. Self-published. (2013)
- Lecture 9
- Noam Nisan, Tim Roughgarden, Éva Tardos, Vijay Vazirani (eds.): Algorithmic Game Theory. Cambridge University Press (2007)
- Lecture 10
- Michael R. Genesereth, Michael Thielscher: General Game Playing (Synthesis Lectures on Artificial Intelligence and Machine Learning) Morgan & Claypool Publishers (2014)
- Lectures 5, 6, and 11
- Bernhard von Stengel: Game Theory Basics. Cambridge University Press (2021)
Subscribe to events of this course (icalendar)
| Lecture | Noncooperative Games in Normal Form | DS3, April 13, 2026 in BEY/E39/U |
Calendar
