Computational Intelligence in Games

Description of the course

This course addresses the basic and advanced topics in computational intelligence and games. This course 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, 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 (5CP) and Master (6CP: including extra programming assignment) students. 

 

Lectures and Tutorials

 

Lectures take place on Tuesdays 13:00 - 14:30 in Room G02-311

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
6 Monte Carlo Tree Search
7 Rolling Horizon Evolutionary Algorithms
  Evolutionary Algorithms (Detailed slides from the course Evolutionary Multi-Objective Optimization(EMO)
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 Wednesdays 13:00 to 15:00 in G22A-020. 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. If your AI performs well during the course, we encourage you to participate in the international competition, which is hosted at the IEEE Conference on Games 2024.

 

Date Exercise Sheet Materials Solution
10.04. No Exercise    
17.04. Introduction AI Competition AI Competition  
24.04. Sheet 1 Sheet 1 Code  Sheet1 solution
01.05. No Exercise    
08.05. Sheet 2    
15.05. Q&A    
22.05. Sheet 3    
27.05. Submission for Intermediate Competition    
29.05. Intermediate Competition, Q&A    
05.06. Sheet 4    
12.06 Q&A    
19.06. Sheet 5    
24.06. Final Submission of your AI    
26.06. Game AI Competition    
03.07. Sheet 6    
10.07. Q&A    

 

 


 

 Past Exams:

 

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

Last Modification: 30.04.2024 - Contact Person: Webmaster