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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 0 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:
formgrouptimeweekroomteacher
lecture          Till Miltzow
Jordi Vermeulen
Note:No up-to-date course description available.
Text below is from year 2019/2020
Contents:

In this course, we look at two ways to use AI for games: training an agent to play a game, and automatically generating (elements of) a game.

The first half of the course will focus on using reinforcement learning to train an agent to play a turn-based game. The second half of the course will focus on different methods of procedural content generation.

Literature:May change!
  • Reinforcement Learning - Suttan and Barto. Available for free online.
  • Procedural Content Generation - Shaker, Togelius and Nelson. Available for free online.
In addition, a selection of papers will be made available.
Course form:Video lectures and two weekly remote interactive sessions.
Exam form:

There will be two homework assignments with theory exercises (H1, H2), and two implementation projects (P1, P2). The final grade will be a weighted sum of the individual grades:

0.25 * H1 + 0.1 * H2 + 0.25 * P1 + 0.4 * P2

The homework exercises will be (partially) peer-graded.

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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