Official course description:

Basic info last published 26/02-19
Course info
ECTS points:
Course code:
Offered to guest students:
Offered to exchange students:
Offered as a single subject:
MSc. Master
M.Sc. in IT, Games
Course semester
Efterår 2018
27 August 2018
28 December 2018
Exam type
ekstern censur
Grade Scale
Exam Language

The goal of the course is to teach the understanding, design, implementation and use of modern artificial intelligence (AI) and computational intelligence (CI) techniques for generating efficient intelligent behaviors in games and other simulation environments. Additional focus will be given to state-of-the-art AI algorithms for improving gameplay experience.


Modern artificial intelligence and computational intelligence have many applications inside and outside computer games. The techniques taught in this course are applicable to games, simulation environments,  robotics, and many other areas.

Students learn a broad understanding of the theoretical, practical and implementation side of AI algorithms.

The course will partly cover the following topics (AI techniques and problems):

AI techniques

  • Finite-state machines
  • Behaviour trees
  • Evolutionary algorithms
  • Artificial neural networksDeep Learning
  • Reinforcement learning
  • Hybrid approaches


  • Pathfinding
  • Non-player character AI
  • Adaptation and learning (off-line and on-line)
  • Player behaviour modelling
  • Player experience modeling General video game playing
  • Dynamic difficulty adjustment

Formal prerequisites
Students must have completed a course on programming such as "Introductory Programming", or "Object-Oriented Programming" and "Efficient AI Programming". Having completed the "Game Development" course is a plus. This course will partly cover AI methodologies of the "Efficient AI Programming" module through a clearer game perspective and introduce state-of-the-art topics of advanced game AI. Moreover the student must always meet the admission requirements of the IT University.
Intended learning outcomes

After the course, the student should be able to:

  • Describe and theorize on the AI algorithms covered in the class.
  • Identify tasks that can be tackled through advanced AI techniques and select the appropriate technique for the problem under investigation.
  • Compare the performance of different AI techniques and reflect on their suitability for different domains
  • Design and implement efficient and robust advanced AI algorithms.
  • Work efficiently in groups
Ordinary exam
Exam type:
D: Written report with oral defence, external (7-trinsskala)
Exam description:
The total duration of the oral examination is 30 minutes per examinee. The written work is a group project report (written work + source code + video production). Group size is normally 2-3 people