Computational Intelligence in Games

Game Theory, Reinforcement Learning and Beyond

(in German: Computational Intelligence in Games )

Intended learning outcomes:
The students will learn the concepts in reinforcement learning, Game Theory and beyond and can apply them to various domains, particularly in Games. The students can program the learned algorithms and will get an understanding about the functionality of the algorithms.

Content:

• This course addresses the basic and advanced topics in the area of computational intelligence and games and contains 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 for a better strategy?
  • – How can we use reinforcement learning as a search mechanism? The application is on board games.
• 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?

Workload:
56 contact hours + 94 h self study

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