Onderwijs Informatica en Informatiekunde
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: |
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| 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! 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. | ||||||||||||||