Computational Intelligence in Games (Bachelor) and Advanced Programming for Computational Intelligence in Games (Master)

This page contains the information for both bachelor and master courses. The advanced programming is only meant for master students.

+++++++++++

For master students taking the course "Advanced Programming for Computational Intelligence in Games":

You must register for this course in lsf and send an email to (sanaz.mostaghim@ovgu.de) with your motivation, why you want to attend this course. Your email must contain [AD-CIG] in the subject. We have a limited capacity of 15 and inform you of the outcome of the selection by Monday 7 April.

Note: Attending the course and the exercises are mandatory.

For better planing, we take both tutorial and lecture timeslots (Tuesdays 11 -13 and 15- 16:30). The first class is on Tuesday 15-16:30 in Room G22A-105. Please note that the location is different than planned in lsf.

+++++++++++

For Bachelor students taking the course "computational Intelligence in Games":

Lectures take place on Tuesdays 15-16:30 in Room G22A-105 and tutorials either on Tuesdays 11-13 or Thusrdays 11 -13. More information is given on the first class on Tuesday 8 April. You must register in lsf.

+++++++++++

This page

Description of the course

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

Part one addresses the basics of 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, and goes beyond the classical reinforcment learning, such as monte-carlo tree search, programing an agent who can pass a Turing test, procedural content generation, decision-making, context steering and learning, etc.

This course will be held in English.

Lectures and Tutorials

Lectures take place for both master and bachelor on Tuesdays 15:00 - 16:30 in Room G22A-105

Slides

Chapter	Slide
1	Organization and Introduction
2	Evolutionary Game Theory
3	Introduction to Reinforcement Learning
4	Dynamic Programming and Monte Carlo Method in RL
5	Temporal Difference Learning
	Deep Reinforcement Learning
6	Monte Carlo Tree Search
7	Rolling Horizon Evolutionary Algorithms
8	Multi-Objective Decision Making and Learning in Games
9	Procedural Content Generation (PCG)

Recorded lectures are on Mediasite: /OVGU/Fakultäten/Informatik (FIN)/Institut für Intelligente Kooperierende Systeme (IKS)/AG Computational Intelligence/Computational Intelligence in Games

Please note that you need to use your URZ account to get access to the recordings.

Exercises

Exercises take place on Tuesdays 11:00 to 13:00 in G22A-111 and Thursdays 11:00 to 13:00 in G22A-105. You must attend one of the two exercise slots, but you are free to choose which one.

During the exercise, you will solve assignments at home and present your solutions during exercise classes. In addition, you will participate in an AI competition, where you will program an AI that can play a game.

This year, the competition is based on the VGC AI Competition. We encourage you to participate in the international competition, which is hosted at the IEEE Conference on Games 2025.

The exercises will be organized via E-Learning. You will be given access to the E-Learning page by us.

Literature

Yannakakis, Georgios N., and Julian Togelius. Artificial Intelligence and Games. Springer, 2018. --> Link
Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998 --> 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

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: Paper1, Paper2, Paper3, Paper4
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