In the last years, Game AI has left the nerd interest area and is now known as incubator for many of the most important AI algorithms of our time. Almost the same methods are employed for beating the world champion in Go as well as solving very complex Chemistry problems for the first time in an automated fashion. We have recently witnessed programs beating human professionals in StarCraft and MOBA games and it is one part of the course to learn what kind of algorithms were used to do it.
Game playing is one pillar of modern Game AI, the others are procedurally generating content (PCG) and modelling players, and of course all these 3 can be combined in various ways. Of course, these methods have ties also to other fields (e.g., behavior trees originated in robotics) and are not constrained to game uses. This renders the course a good introduction also to general AI.
Getting an overview over and a fundamental understanding of the algorithms that drive Game AI and thus also AI progress, e.g. Monte Carlo Tree Search, Deep Learning, and behavior trees;
Learn how to employ these methods especially in the context of Procedural Content Generation and General Video Game Playing;
Learn how to use human feedback for generation and adaptation processes.
The most recent timetable can be found on the students' website.
Mode of instruction
Lectures: online lectures to be available before the regular lecture slot, questioning hours during the regular lecture slot.
Lab sessions: online questioning hours in the regular lab slot.
Total hours of study: 168 hrs. (= 6 EC)
Lectures: 26:00 hrs.
Practicals: 26:00 hrs.
Group assignments: 113:00 hrs.
Examination: 3:00 hrs.
Individual homework assignment (20%)
Group project on general video game AI (35%)
Free group project in the context of the lecture topics (45%)
Diego Perez-Liebana and Simon M. Lucas and Raluca D. Gaina and Julian Togelius and Ahmed Khalifa and Jialin Liu: General Video Game Artificial Intelligence, Morgan and Claypool Publishers, 2019.
You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.
Please also register for the course in Blackboard.
Lecturer: dr. M. Preuss
Teaching assistant: Matthias Müller-Brockhausen (skype: firstname.lastname@example.org)
Teaching Assistant: Laduona Dai (skype: email@example.com)
While the course touches on many algorithms, it is rather practically oriented. The most important questions are a) how does it work, and b) how can it be used in practice. Due to the availability of suitable software, group assignments shall mostly use Java or Python. Have a look at IEEE CoG 2019 Competitions to get an idea of what we deal with.