Foundations of Game AI, BSc
A report with the findings of the project, Github repository with analysis code Mixed exam 1 : Individual and joint student presentThrough this course, the students will learn about the aspects of game programming commonly involving artificial intelligence methods, which methods are used and how to implement them.
The course will go through a series of areas of application of artificial intelligence in games.
Each area will be discussed and formalised as an AI problem. For each of these problems, the students will learn one or multiple algorithms that are used as a solution in modern game development and how to implement them.
The main areas of game that will be covered are: input and output representation, pre-processing, path finding, NPC behaviour and procedural content generation.
Students must have experience with and be comfortable with programming, and be capable of independently implementing algorithms from descriptions. This corresponds to at least having passed an introductory programming course, and preferably also an intermediate-level programming course. The course will contain compulsory programming.
Intended learning outcomes
After the course, the student should be able to:
- Analyse a gameplay or game technology related problem in terms of an artificial intelligence problem
- Given a formalised AI problem identify the most appropriate artificial intelligence algorithm to solve it
- Compare different AI solutions in terms of effectiveness and computational efficiency.
- Describe AI algorithms for animation, motion, game playing and procedural content generation and discuss their potential implementations
- Combine multiple algorithms to create complex solutions (e.g. agent behaviours).
- Given a new problem description within the context of Game AI, theorise a potential solution using one more artificial intelligence algorithms
Ordinary examExam type:
B: Oral exam, External (7-point scale)
B22: Oral exam with no time for preparation.