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Advanced Programming in Computational Intelligence in Games

(in German: Advanced Programming in Computational Intelligence in Games )

Module-ID: FIN-INF-120495
Link: LSF
Responsibility: Sanaz Mostaghim
Lecturer: Sanaz Mostaghim
Classes:
  • Lecture Advanced Programming in Computational Intelligence in Games
  • Tutorials Advanced Programming in Computational Intelligence in Games
 
Applicability in curriculum: - M.Sc. INF: Informatik
- M.Sc. INGINF: Informatik
- M.Sc. WIF: Informatik
- M.Sc. DKE: Applied Data Science
- M.Sc. VC: Computer Science

Abbreviation

AP-CIG

Credit Points

6

Semester

Summer

Term

ab 1.

Duration

1 Semester

Language

english

Level

Master

Intended learning outcomes:
Students ...

  • carry out a large programming project using the concepts from the theoretical part of the course.
  • have a full understanding of the algorithms of computational Intelligence in Games.
  • learn to develop new ideas and apply them to new problems in the context of AI and Games.
  • learn to program the methodologies from the lectures in teams.

Content:
This course addresses the basic and advanced topics in the area of computational intelligence and games and contains three parts: Part 1 addresses the basics in Evolutionary Game Theory (EGT). In this part you will learn about simple games such as scissors/rock/paper and the focus on the strategies for playing games. Part 2 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 for a better strategy?
  • How can we use reinforcement learning as a search mechanism?
The application is on board games. Part 3 contains the advanced topics in games and artificial intelligence How can we consider physical constraints of a spaceship while moving in an unknown terrain. Other topics are Monte Carlo Tree Search, Procedural Content Generation, Decision Making and Multi-Objective Learning.

Workload:
56 contact hours + 94h self study

Pre-examination requirements: Type of examination: Teaching method / lecture hours per week (SWS):

  • Hausarbeit (home work containing programming)
  • Exercises and Tutorials
  • Klausur (written Exam) 120 Minuten

  • lectures (2 SWS)
  • exercise classes (2 SWS)
Prerequisites according to examination regulations: Recommended prerequisites:

none

Programming experience

Media: Literature:

  • ­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
  • 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

Comments:

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