AI for game technology

Course goals

After the end of the course, the student will be able to:

• explain and differentiate different game AI approaches.
• explain core concepts from AI playing games.
• explain core concepts from content generation.
• explain core concepts from player modeling.
• choose suitable game AI approaches for solving specific game design and development tasks.
• explain and differentiate different player modeling approaches.
• design and implement player modeling approaches.
• present game AI approaches in reports and presentations.

Assessment
The assessment is based on

• a mid-term exam (40% of the final mark)
• a group project including a project report, video, and presentation (60%)

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Prerequisites
Please be aware that some of the projects are individual; it is expected that you have enough programming experience to complete these projects.
If you are an inexperienced programmer, you may need to spend extra time on this course.

Content

The course deals with AI topics in the context of games. AI has been an essential component in games for decades.
It is used for a variety of use cases, including as a tool for research the development of games as well as an integrated component in games.
In this course, we will cover such use cases, including about AI-based approaches to play games, content generation, as well as player modeling.

Course form
Weekly two sessions blending lectures, practical assignments, and Q&A, as well as weekly individual assignments.

Literature

• Yannakakis, Georgios N., and Julian Togelius, "Artificial intelligence and games" vol. 2. New York: Springer, 2018.
• El-Nasr, Magy Seif, Anders Drachen, and Alessandro Canossa, "Game analytics". Springer London Limited, 2016.
• A selection of academic papers that will be linked in the course.