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 as follows:
- Thursdays, DS2, APB/E007
- Thursdays, DS4, APB/E001
- Thursdays, DS5, APB/E006
OPAL
There is an OPAL course that we use to reach you in case exercise sessions or lectures have to be cancelled on short notice. Please register for the lecture and your respective exercise session to stay informed.
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/Erasmus 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 | File |
| Exercise | Exercises 1 | File | |
| Lecture | Normal-Form Games: Mixed Strategies | DS3, April 20, 2026 in BEY/E39/U | File |
| Exercise | Exercises 2 | File | |
| Lecture | Complexity and Correlated Equilibria | DS3, April 27, 2026 in BEY/E39/U | File |
| Exercise | Exercises 3 | File | |
| Lecture | Sequential Games with Perfect Information | DS3, May 4, 2026 in BEY/E39/U | File |
| Exercise | Exercises 4 | File | |
| Lecture | Playing Games: Alpha-Beta Tree Search | DS3, May 11, 2026 in BEY/E39/U | File |
| No session | Public holiday (no exercise sessions) |
Calendar
