Algorithmic Game Theory

From International Center for Computational Logic

Algorithmic Game Theory

Course with SWS 2/2/0 (lecture/exercise/practical) in SS 2024

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:

Exercise sessions are offered at the following times:

  • Tuesdays, DS6, APB/E006
  • Thursdays, DS4, APB/E005
  • Thursdays, DS5, APB/E006

Exercises start in the week of the first lecture, i.e. on 16th/18th April.

In the week of 6th to 10th May, there will be an exceptional virtual exercise session on Wednesday, 8th May, 13:00 via Zoom.

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).

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 Ms. Ramona Behling.
  • 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, 11, and 12
  • Richard Alan Gillman, David Housman: Game Theory. A Modeling Approach. CRC Press (2019)
    • Lectures 1, 2, 3, and 9
  • Stuart J. Russell, Peter Norvig: Artificial Intelligence. A Modern Approach (Global Edition). Pearson (2021) (Chapter 6: Adversarial Search and Games)
    • Lectures 4 and 5
  • Todd W. Neller, Marc Lanctot: An Introduction to Counterfactual Regret Minimization. Self-published. (2013)
    • Lecture 8
  • Noam Nisan, Tim Roughgarden, Éva Tardos, Vijay Vazirani (eds.): Algorithmic Game Theory. Cambridge University Press (2007)
    • Lecture 9
  • Michael R. Genesereth, Michael Thielscher: General Game Playing (Synthesis Lectures on Artificial Intelligence and Machine Learning) Morgan & Claypool Publishers (2014)
    • Lectures 4, 5, and 10
  • 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 15, 2024 in SCH/A316 File
Exercise Noncooperative Games in Normal Form File
Lecture Normal-Form Games: Mixed Strategies DS3, April 22, 2024 in SCH/A316 File
Exercise Normal-Form Games: Mixed Strategies File
Lecture Sequential Games with Perfect Information DS3, April 29, 2024 in SCH/A316 File
Exercise Sequential Games with Perfect Information File
Lecture Playing Games: Alpha-Beta Tree Search DS3, May 6, 2024 in SCH/A316 File
Exercise Minimax and Alpha-Beta Tree Search File
Lecture Playing Games: Monte Carlo Tree Search DS3, May 13, 2024 in SCH/A316 File
Exercise Monte Carlo Tree Search File


Calendar