Computational Intelligence in Games

We uploaded the sixth exercise sheet. It will be due to the 29th June/2th/4th July!

We finished the  Equation Sheet containing all symbols, equations and algorithms.

Please state your preferences for the official competition evaluation



This course addresses the basic and advanced topics in the area of computational intelligence and games. This course has three parts:

Part one addresses the basics in Evolutionary Game Theory (EGT). In this part you will learn about simple games such as scissors/rock/paper and the main focus on the strategies for playing games.

Part two is about learning agents and we focus on reinforcement learning mechanisms. There are three questions for games:

How can we use the information from a search mechanism to learn? 
How can we use reinforcement learning to find a better strategy?
How can we use reinforcement learning as a search mechanism? 

Part three contains the advanced topics in games and artificial intelligence such as how can we program an agent who can pass a Turing test? how can we consider physical constraints of a spaceship while moving in an unknown terrain? etc. 

This course will be held in English and is for Bachelor and Master students.


Lectures and Tutorials


 Lecture:  Thursdays 9:15-10:45 in G29 307

 Exercise Class 1: Monday 9am-11am in G02-111

 Exercise Class 2: Friday 1pm - 3pm in G02-109

 Exercise Class 3: Wednesday 1pm - 3pm in G22A-020


Conditions for Certificates (Scheine) and Exams


Certificate (Übungsschein): There are assignment sheets published every week. Assignments the solutions of which you want to present in the next exercise lecture have to be ticked beforehand on a votation sheet that is handed our prior to every exercise lecture. If ticked, you may be asked to present your solution in front of class. The solutions need not necessarily be completely correct, however, it should become obvious that you treated the assignment thoroughly. You are granted the certificate (Schein), if (and only if) you

  • ticked at least two thirds of the assignments,
  • presented at least one time a solution during the exercise,
  • solve the programming assignment, and
  • pass the exam

Exam: If you intend to finish the course with an exam, your are required to meet the certificate conditions. There will be a written exam after the curse.

Master Students: Master students will be required to solve an additional programming assignment to account for the 6th Credit Point.



We compiled a Cheat Sheet containing all symbols, equations and algorithms. We hope this overview helps you during your study of the course topics.

0 Organisation Slides 2x2Slides    
1 Introduction Slides 2x2Slides Script  
2 Evolutionary Game Theory Slides 2x2Slides Script minor update on 22.04.2018, corrected an error on slide 60 and 65
2.5 Applications of Game Theory Slides 2x2Slides Script  
3 Introduction to Reinforcement Learning Slides 2x2Slides Script minor update on 27.04.2018, corrected an error on slide 32
4 Dynamic Programming and Monte Carlo Method in RL Slides 2x2Slides Script major update on 27.04.2018, corrected algorithm names, added overview slide, slide 25
5 Temporal Difference Learning Slides 2x2Slides Script  minor update on 16.05.2018, corrected some numbers on slide 18
6 Monte Carlo Tree Search Slides 2x2Slides Script  
6.5 Monte Carlo Tree Search in AlphaGo Slides 2x2Slides Script  
7 Rolling Horizon Evolutionary Algorithms Slides 2x2Slides Script  
8 Multi-Objective Decision Making and Learning in Games Slides 2x2Slides Script  minor update on 09.06.2018, corrected equation and example on slide 36/37
9 Procedural Content Generation (PCG) Slides 2x2Slides Script  
10 General Game AI Slides 2x2Slides Script  
11 CIG in the Industry Slides 2x2Slides Script  


 Videos and simulations related to lectures



 Recorded Lectures


  • Recordings of last years lectures can be found here:
  • Note that contents may change and watching the videos can not fully substitute visiting the lectures or checking the updated script.
  • Please note that you require your URZ account to be able to see the recordings.

 Exercise Classes:

 Exercise Class 1: Monday 9am-11am in G02-111

 Exercise Class 2: Wednesday 1pm - 3pm in G22A-020

 Exercise Class 3: Friday 1pm - 3pm in G02-109


Exercise Sheets Assignments:

ID Topic Due Date per Group Exercise Sheet Additional Files
     Monday  Wednesday   Friday      
1 Evolutionary Game Theory 23.04. 25.04. 20.04. PDF ---
2 ESS, Game Policy, MDP, RL 07.05. 09.05. 04.05. PDF ---
3 Monte Carlo, Temporal Difference Learning 28.05. 23.05. 25.05. PDF ---
4 Action Selection, Monte Carlo Tree Search 04.06. 06.06. 01.06. PDF ZIP
5 Evolutionary Algorithms 18.06. 20.06. 15.06. PDF ---
6 Multi-Objective Optimization  02.07. 04.07. 29.06 PDF


 Please state your preferences for the official competition evaluation


 Programming Assignment:


You can find information on the programming assignment under:


 Past Exam:


Exam WS 2015/2016




  • Yannakakis, Georgios N., and Julian Togelius. Artificial Intelligence and Games. Springer, 2018. --> Link

  • Nowak, Martin, Evolutionary dynamics : exploring the equations of life, Cambridge, Mass. [u.a.] : Belknap Press of Harvard Univ. Press , 2006 --> Link to OvGU Library
  • Ian Millington and John Funge, Artificial Intelligence for Games, CRC Press, 2009
  • Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998
  • T. L. Vincent and J. L. Brown, Evolutionary Game Theory, Natural Selection and Darwinian Dynamics, Cambridge University Press, 2012
  • Jorgen W. Weibull, Evolutionary Game Theory, MIT Press, 1997
  • Thomas Vincent, Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics, Cambridge University Press, 2005
  • Josef Hofbauer, Karl Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, 1998
  • Kalyanmoy Deb, Multi-Objective Optimization using Evolutionary Algorithms, Wiley, 2001
  • Literature about PCG: Paper1Paper2Paper3Paper4
  • Kruse, Borgelt, Klawonn, Moewes, Ruß, Steinbrecher, Computational Intelligence, Vieweg+Teubner, Wiesbaden, 2011
  • Ines Gerdes, Frank Klawonn, Rudolf Kruse, Evolutionäre Algorithmen, Vieweg, Wiesbaden, 2004
  • Zbigniew Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin, 1998


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