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AI for game technology

Course code:INFOMAIGT
Credits:7.5 ECTS
History:This course was formerly known as Games and agents (INFOMGMAG). You can only do one of these courses.
Period:period 4 (week 17 through 26, i.e., 26-4-2021 through 2-7-2021; retake week 28)
Timeslot:C
Participants:up till now 39 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:
formgrouptimeweekroomteacher
lecture          Till Miltzow
Jordi Vermeulen
Contents:

In this course, we look at ways in which AI techniques can be applied to games. The course is divided into two parts. In the first part, we look at how an AI can learn to play a turn-based game through reinforcement learning. We discuss techniques such as bandit strategies, Monte Carlo tree search and neural networks. While these techniques are applicable to a wide range of problems, we focus in particular on how they can be applied to games.

In the second part of the course, we look at how AI techniques can help us create (parts of) games through procedural content generation. We discuss techniques such as grammar-based systems, Markov chains, genetic algorithms and generative adversarial networks, and show how they can be used to generate a variety of different kinds of (game) content.

Both parts of the course have a heavy focus on programming assignments and experimentation. The implementation projects will be mostly written in Python. 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.

The following skills will be assumed in this course:

  • the ability to program in Python
  • the ability to set up and train a neural network.

Have a look at our introduction video.

Come to Gather Town on Friday 4pm the 26th of February, to ask any questions you may have about the course.

Literature:
  • Reinforcement Learning - Suttan and Barto. Available for free online.
  • A selection of papers that will be linked during the course.
Course form:We will have recorded lectures and interactive Q&A sessions, poster sessions.
Exam form:Seven programming assignments (90% of grade), small weekly exercises (10% of grade).
Minimum effort to qualify for 2nd chance exam:To qualify for the retake exam, the grade of the original must be at least 4.
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