Official course description, subject to change:
Preliminary info last published 25/10-19

Modern Artificial Intelligence

Course info
Language:
English
ECTS points:
15
Course code:
KGMOARI1KU
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
21250 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
Master of Science in Information Technology (Games)
Staff
Course semester
Semester
Efterår 2020
Start
24 August 2020
End
31 January 2021
Abbreviation
20202
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

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.

Description

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


Tasks/Problems

  • 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

Intended learning outcomes

After the course, the student should be able to:

  • Theorize about and describe 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: Submission of written work with following oral, external (7-trinsskala)
Exam variation:
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted.
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.

Form of group exam: Mixed Exam 2